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Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).
Yufu Zang; Fancong Meng; Roderik Lindenbergh; Linh Truong-Hong; Bijun Li. Deep Localization of Static Scans in Mobile Mapping Point Clouds. Remote Sensing 2021, 13, 219 .
AMA StyleYufu Zang, Fancong Meng, Roderik Lindenbergh, Linh Truong-Hong, Bijun Li. Deep Localization of Static Scans in Mobile Mapping Point Clouds. Remote Sensing. 2021; 13 (2):219.
Chicago/Turabian StyleYufu Zang; Fancong Meng; Roderik Lindenbergh; Linh Truong-Hong; Bijun Li. 2021. "Deep Localization of Static Scans in Mobile Mapping Point Clouds." Remote Sensing 13, no. 2: 219.
Heritage documentation is implemented by digitally recording historical artifacts for the conservation and protection of these cultural heritage objects. As efficient spatial data acquisition tools, laser scanners have been widely used to collect highly accurate three-dimensional (3D) point clouds without damaging the original structure and the environment. To ensure the integrity and quality of the collected data, field inspection (i.e., on-spot checking the data quality) should be carried out to determine the need for additional measurements (i.e., extra laser scanning for areas with quality issues such as data missing and quality degradation). To facilitate inspection of all collected point clouds, especially checking the quality issues in overlaps between adjacent scans, all scans should be registered together. Thus, a point cloud registration method that is able to register scans fast and robustly is required. To fulfill the aim, this study proposes an efficient probabilistic registration for free-form cultural heritage objects by integrating the proposed principal direction descriptor and curve constraints. We developed a novel shape descriptor based on a local frame of principal directions. Within the frame, its density and distance feature images were generated to describe the shape of the local surface. We then embedded the descriptor into a probabilistic framework to reject ambiguous matches. Spatial curves were integrated as constraints to delimit the solution space. Finally, a multi-view registration was used to refine the position and orientation of each scan for the field inspection. Comprehensive experiments show that the proposed method was able to perform well in terms of rotation error, translation error, robustness, and runtime and outperformed some commonly used approaches.
Yufu Zang; Bijun Li; Xiongwu Xiao; Jianfeng Zhu; Fancong Meng. An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection. ISPRS International Journal of Geo-Information 2020, 9, 759 .
AMA StyleYufu Zang, Bijun Li, Xiongwu Xiao, Jianfeng Zhu, Fancong Meng. An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection. ISPRS International Journal of Geo-Information. 2020; 9 (12):759.
Chicago/Turabian StyleYufu Zang; Bijun Li; Xiongwu Xiao; Jianfeng Zhu; Fancong Meng. 2020. "An Efficient Probabilistic Registration Based on Shape Descriptor for Heritage Field Inspection." ISPRS International Journal of Geo-Information 9, no. 12: 759.
Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m.
Hongjuan Zhang; Wenzhuo Li; Chuang Qian; Bijun Li. A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching. Remote Sensing 2020, 12, 2607 .
AMA StyleHongjuan Zhang, Wenzhuo Li, Chuang Qian, Bijun Li. A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching. Remote Sensing. 2020; 12 (16):2607.
Chicago/Turabian StyleHongjuan Zhang; Wenzhuo Li; Chuang Qian; Bijun Li. 2020. "A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching." Remote Sensing 12, no. 16: 2607.
Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct.
Chuang Qian; Hongjuan Zhang; Wenzhuo Li; Jian Tang; Hui Liu; Bijun Li. Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles. Remote Sensing 2020, 12, 949 .
AMA StyleChuang Qian, Hongjuan Zhang, Wenzhuo Li, Jian Tang, Hui Liu, Bijun Li. Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles. Remote Sensing. 2020; 12 (6):949.
Chicago/Turabian StyleChuang Qian; Hongjuan Zhang; Wenzhuo Li; Jian Tang; Hui Liu; Bijun Li. 2020. "Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles." Remote Sensing 12, no. 6: 949.
As an important role in autonomous vehicles or advanced driving assistance systems, lane detection uses the onboard camera high up on the windshield to provide the vehicle’s lateral offset within its own lane in a real-time, low-cost way. In this paper, we propose an efficient, robust lane detection method based on histogram of oriented vanishing points. First, the lane features are extracted by symmetrical local threshold. Then, the lines are generated from oriented vanishing points. The lines crossing most features are selected and oriented vanishing points are updated by the overlap between features and selected lines. Last step will be repeated for getting stable oriented vanishing points. Therefore, the last selected lines are most likely to be lane lines. Finally, Validate and select the best lane lines. The proposed method has been tested on a public dataset. The experimental results show that the method can improve robustness under real-time automated driving.
Shizeng Chen; Bijun Li; Yuan Guo; Jian Zhou. Lane Detection Based on Histogram of Oriented Vanishing Points. Communications in Computer and Information Science 2020, 3 -11.
AMA StyleShizeng Chen, Bijun Li, Yuan Guo, Jian Zhou. Lane Detection Based on Histogram of Oriented Vanishing Points. Communications in Computer and Information Science. 2020; ():3-11.
Chicago/Turabian StyleShizeng Chen; Bijun Li; Yuan Guo; Jian Zhou. 2020. "Lane Detection Based on Histogram of Oriented Vanishing Points." Communications in Computer and Information Science , no. : 3-11.
With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.
Ling Zheng; Huashan Song; Bijun Li; Hongjuan Zhang; Song; Li; Zhang; And Hongjuan Zhang. Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy. ISPRS International Journal of Geo-Information 2019, 8, 416 .
AMA StyleLing Zheng, Huashan Song, Bijun Li, Hongjuan Zhang, Song, Li, Zhang, And Hongjuan Zhang. Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy. ISPRS International Journal of Geo-Information. 2019; 8 (9):416.
Chicago/Turabian StyleLing Zheng; Huashan Song; Bijun Li; Hongjuan Zhang; Song; Li; Zhang; And Hongjuan Zhang. 2019. "Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy." ISPRS International Journal of Geo-Information 8, no. 9: 416.
Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review.
Ling Zheng; Bijun Li; Bo Yang; Huashan Song; Zhi Lu. Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey. Sustainability 2019, 11, 4511 .
AMA StyleLing Zheng, Bijun Li, Bo Yang, Huashan Song, Zhi Lu. Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey. Sustainability. 2019; 11 (16):4511.
Chicago/Turabian StyleLing Zheng; Bijun Li; Bo Yang; Huashan Song; Zhi Lu. 2019. "Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey." Sustainability 11, no. 16: 4511.
An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.
Chuang Qian; Hongjuan Zhang; Jian Tang; Bijun Li; Hui Liu. An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping. Sensors 2019, 19, 1742 .
AMA StyleChuang Qian, Hongjuan Zhang, Jian Tang, Bijun Li, Hui Liu. An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping. Sensors. 2019; 19 (7):1742.
Chicago/Turabian StyleChuang Qian; Hongjuan Zhang; Jian Tang; Bijun Li; Hui Liu. 2019. "An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping." Sensors 19, no. 7: 1742.
Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.
Bo Yang; Sheng Zhang; Yan Tian; Bijun Li. Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics. Sensors 2019, 19, 1728 .
AMA StyleBo Yang, Sheng Zhang, Yan Tian, Bijun Li. Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics. Sensors. 2019; 19 (7):1728.
Chicago/Turabian StyleBo Yang; Sheng Zhang; Yan Tian; Bijun Li. 2019. "Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics." Sensors 19, no. 7: 1728.
Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.
Qingquan Li; Jian Zhou; Bijun Li; Yuan Guo; Jinsheng Xiao. Robust Lane-Detection Method for Low-Speed Environments. Sensors 2018, 18, 4274 .
AMA StyleQingquan Li, Jian Zhou, Bijun Li, Yuan Guo, Jinsheng Xiao. Robust Lane-Detection Method for Low-Speed Environments. Sensors. 2018; 18 (12):4274.
Chicago/Turabian StyleQingquan Li; Jian Zhou; Bijun Li; Yuan Guo; Jinsheng Xiao. 2018. "Robust Lane-Detection Method for Low-Speed Environments." Sensors 18, no. 12: 4274.
High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM.
Ling Zheng; Bijun Li; Hongjuan Zhang; Yunxiao Shan; Jian Zhou. A High-Definition Road-Network Model for Self-Driving Vehicles. ISPRS International Journal of Geo-Information 2018, 7, 417 .
AMA StyleLing Zheng, Bijun Li, Hongjuan Zhang, Yunxiao Shan, Jian Zhou. A High-Definition Road-Network Model for Self-Driving Vehicles. ISPRS International Journal of Geo-Information. 2018; 7 (11):417.
Chicago/Turabian StyleLing Zheng; Bijun Li; Hongjuan Zhang; Yunxiao Shan; Jian Zhou. 2018. "A High-Definition Road-Network Model for Self-Driving Vehicles." ISPRS International Journal of Geo-Information 7, no. 11: 417.
The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.
Bijun Li; Yuan Guo; Jian Zhou; Yi Cai. A Data Correction Algorithm for Low-Frequency Floating Car Data. Sensors 2018, 18, 3639 .
AMA StyleBijun Li, Yuan Guo, Jian Zhou, Yi Cai. A Data Correction Algorithm for Low-Frequency Floating Car Data. Sensors. 2018; 18 (11):3639.
Chicago/Turabian StyleBijun Li; Yuan Guo; Jian Zhou; Yi Cai. 2018. "A Data Correction Algorithm for Low-Frequency Floating Car Data." Sensors 18, no. 11: 3639.
Based on the hardware and sensors of image acquisition, the noise in the image has been easily generated. In this paper, an improved method of image decompression has proposed the shortcoming of the above-mentioned hardware algorithm. The traditional filter desiccation algorithm can only remove one or two specific noises, and it is not effective for other types. We combine some excellent neural network models. In this paper, an image mixing noise removal algorithm based on convolution nerve has been mentioned. Aiming at realizing the super-resolution of the image, the deconvolution layer can be used only to enlarge the image. The magnification factor is the step of deconvolution. This paper aims to eliminate the interference of the image noise. The effect of magnification on the deconvolution layer is impossible. The results of experimental test show that the algorithm achieves a good noise removal effect and is suitable for various mixed noise images. The algorithm used in this paper improves the subjective visual effect and objective evaluation index.
Ling Ding; Huyin Zhang; Jinsheng Xiao; Bijun Li; Shejie Lu; Mohammad Norouzifard. An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN. Neural Computing and Applications 2018, 31, 325 -336.
AMA StyleLing Ding, Huyin Zhang, Jinsheng Xiao, Bijun Li, Shejie Lu, Mohammad Norouzifard. An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN. Neural Computing and Applications. 2018; 31 (S1):325-336.
Chicago/Turabian StyleLing Ding; Huyin Zhang; Jinsheng Xiao; Bijun Li; Shejie Lu; Mohammad Norouzifard. 2018. "An improved image mixed noise removal algorithm based on super-resolution algorithm and CNN." Neural Computing and Applications 31, no. S1: 325-336.
Aiming at limiting drawbacks of denoising algorithms that can only remove one or two specific types of noise (and which are inefficient for other types), we propose a combined neural-network model for mixed-noise removal in images. Nine convolutional layers are adapted, and noisy images are trained through feature extraction, shrinking, nonlinear mapping, expanding, and reconstruction. Experimental results show that the algorithm achieves better denoising results and is more suitable than other algorithms for dealing with different types of mixed noise in images. Subjective visual effects and an objective evaluation demonstrate the achieved improvements.
Ling Ding; Huyin Zhang; Bijun Li; Jian Zhou; Wenhao Gu. Mixed-Noise Removal in Images Based on a Convolutional Neural Network. Privacy Enhancing Technologies 2018, 453 -464.
AMA StyleLing Ding, Huyin Zhang, Bijun Li, Jian Zhou, Wenhao Gu. Mixed-Noise Removal in Images Based on a Convolutional Neural Network. Privacy Enhancing Technologies. 2018; ():453-464.
Chicago/Turabian StyleLing Ding; Huyin Zhang; Bijun Li; Jian Zhou; Wenhao Gu. 2018. "Mixed-Noise Removal in Images Based on a Convolutional Neural Network." Privacy Enhancing Technologies , no. : 453-464.
Lane detection algorithm based on monocular camera is one of the most popular methods in recent years, which can meet the requirement of real-time and robust for autonomous vehicle. In this way, the position of lane markers can be transferred from perspective space to road space base on the planar road assumption. However, large numbers of road scenes, especially the up and down slope road environment, cannot meet this requirement.In this paper, we propose a multiple vanishing point detection method to reconstruct the road space in slope scenes. In order to improve the accuracy of vanishing point estimation, the road images are decomposed into near and far regions. We extract candidate lane markers in near region by using multiscale convolution kernel and Hough Transform at first. Then, the lane markers in far region can be detected based on the result of near region. At last, different vanishing points are extracted in near and far regions. With the help of a vanishing point based on camera model, we can project both of near and far regions into road space. The experiment is conducted on our self-driving car `TuLian' in campus environment.
Bijun Li; Yuan Guo; Jian Zhou; Yi Cai; Jinsheng Xiao; Weicheng Zeng. Lane Detection and Road Surface Reconstruction Based on Multiple Vanishing Point & Symposia. 2018 IEEE Intelligent Vehicles Symposium (IV) 2018, 209 -214.
AMA StyleBijun Li, Yuan Guo, Jian Zhou, Yi Cai, Jinsheng Xiao, Weicheng Zeng. Lane Detection and Road Surface Reconstruction Based on Multiple Vanishing Point & Symposia. 2018 IEEE Intelligent Vehicles Symposium (IV). 2018; ():209-214.
Chicago/Turabian StyleBijun Li; Yuan Guo; Jian Zhou; Yi Cai; Jinsheng Xiao; Weicheng Zeng. 2018. "Lane Detection and Road Surface Reconstruction Based on Multiple Vanishing Point & Symposia." 2018 IEEE Intelligent Vehicles Symposium (IV) , no. : 209-214.
Image denoising requires taking into account the dependence of the noise distribution on the original image, and the performance of most video denoising algorithms depend on the noise parameters of noisy video, which is particularly important for the estimation of noise parameters. We propose a novel noise estimation method which combines principal component analysis (PCA) and variance-stabilizing transformation (VST), and extend the noise estimation to mixed noise estimation. We also introduce the excess kurtosis to ensure the accuracy of noise estimation and estimate the parameters of VST by minimizing the excess kurtosis of noise distribution. Subjective and objective results show that proposed noise estimation combining with classic video denoising algorithms obtains better effects and make video denoising more widely in application.
Ling Ding; Huying Zhang; Bijun Li; Jinsheng Xiao; Jian Zhou. Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation. Computer Vision 2017, 58 -69.
AMA StyleLing Ding, Huying Zhang, Bijun Li, Jinsheng Xiao, Jian Zhou. Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation. Computer Vision. 2017; ():58-69.
Chicago/Turabian StyleLing Ding; Huying Zhang; Bijun Li; Jinsheng Xiao; Jian Zhou. 2017. "Image Noise Estimation Based on Principal Component Analysis and Variance-Stabilizing Transformation." Computer Vision , no. : 58-69.
Zhixiang Fang; Ling Li; Bijun Li; Jingwei Zhu; Qingquan Li; Shengwu Xiong. An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night. International Journal of Geographical Information Science 2017, 31, 2020 -2044.
AMA StyleZhixiang Fang, Ling Li, Bijun Li, Jingwei Zhu, Qingquan Li, Shengwu Xiong. An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night. International Journal of Geographical Information Science. 2017; 31 (10):2020-2044.
Chicago/Turabian StyleZhixiang Fang; Ling Li; Bijun Li; Jingwei Zhu; Qingquan Li; Shengwu Xiong. 2017. "An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night." International Journal of Geographical Information Science 31, no. 10: 2020-2044.
We present an improved RRT* to blend lane information and avoid obstacles in this paper. Unlike most of other improved RRT*, this paper attach great importance to the convergent goal of RRT*. We consider the condition that there exists a reference path, maybe not the shortest path, but the environment requires the vehicle to follow, such as a lane center. Compared with standard RRT* applied to differential situation, we first add a TesttoGoal procedure to improve the convergent speed and also make sure the path can reach the goal pose but not the goal region to promise the safety of autonomous vehicle. One of the key characteristic of our improved algorithm is to employ a fast clothoid fitting method into RRT* to enable us to control the curvature. Another important modification is the heuristic sampling method that makes our algorithm can converge to lane center. We evaluate our algorithm with a real lane to demonstrate the effect of our modifications.
Yun-Xiao Shan; Bi-Jun Li; Xiaomin Guo; Jian- Zhou; Ling- Zheng. A considering lane information and obstacle-avoidance motion planning approach. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) 2014, 16 -21.
AMA StyleYun-Xiao Shan, Bi-Jun Li, Xiaomin Guo, Jian- Zhou, Ling- Zheng. A considering lane information and obstacle-avoidance motion planning approach. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). 2014; ():16-21.
Chicago/Turabian StyleYun-Xiao Shan; Bi-Jun Li; Xiaomin Guo; Jian- Zhou; Ling- Zheng. 2014. "A considering lane information and obstacle-avoidance motion planning approach." 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) , no. : 16-21.
This paper presents a personification of an autonomous vehicle control strategy to accomplish curved path tracking, including three levels: curve fitting, curve discretization, and designing tracking controller. Bezier curve fitting is used to fit the path generated by RRT to create a smooth path. The purpose of discretization is to discrete the curve into points, and the Douglas-Peucker algorithm widely used in the GIS field is employed to discrete the curve intoa set of lines. Controller design is on the basis of abstractions of the human decision thinking; a P controller combined with a PD controller mimics human driving behavior. Simulation results show that: the strategy on the condition of low speed (1-10m/s) demonstrates good speed robustness and tracking accuracy.
Yun-Xiao Shan; Bi-Jun Li; Jian Zhou; Yue Zhang; Teng Li. An Autonomous Vehicle Control Strategy to Imitate Human Behavior Applied in Path Tracking. CICTP 2014 2014, 322 -333.
AMA StyleYun-Xiao Shan, Bi-Jun Li, Jian Zhou, Yue Zhang, Teng Li. An Autonomous Vehicle Control Strategy to Imitate Human Behavior Applied in Path Tracking. CICTP 2014. 2014; ():322-333.
Chicago/Turabian StyleYun-Xiao Shan; Bi-Jun Li; Jian Zhou; Yue Zhang; Teng Li. 2014. "An Autonomous Vehicle Control Strategy to Imitate Human Behavior Applied in Path Tracking." CICTP 2014 , no. : 322-333.
In order to realize the flexible zooming and adaptive display of navigable electronic maps on mobile devices of limited display size, maps at different spatial resolutions are required. Multiple resolution database (MRDB) is employed to manage these multi-resolution maps in previous research. In this paper, a multi-resolution navigable database is designed to manage the multi-resolution navigable electronic maps on the basis of MRDB theories and GDF (geographic data file) conceptual data model. Several road network generalization operators to derive low resolution maps from high resolution maps are presented to enrich the database. Most importantly, to realize the incremental update of the multi-resolution navigable databases, an approach for propagating update across navigable maps at different resolutions is proposed to consistently update the database in an incremental way.
Bijun Li; Weifeng Zhao; Bisheng Yang; Qingquan Li. An MRDB approach for propagating updates across navigable maps at different resolutions. 2009 17th International Conference on Geoinformatics 2009, 1 -6.
AMA StyleBijun Li, Weifeng Zhao, Bisheng Yang, Qingquan Li. An MRDB approach for propagating updates across navigable maps at different resolutions. 2009 17th International Conference on Geoinformatics. 2009; ():1-6.
Chicago/Turabian StyleBijun Li; Weifeng Zhao; Bisheng Yang; Qingquan Li. 2009. "An MRDB approach for propagating updates across navigable maps at different resolutions." 2009 17th International Conference on Geoinformatics , no. : 1-6.