The Lidar Simultaneous Localization and Mapping (Lidar-SLAM) processes the point cloud from the Lidar and accomplishes location and mapping. Lidar SLAM is usually divided to front-end odometry and back-end optimization, which can run parallelly to improve computation efficiency. The font-end odometry estimates the Lidar motion through processing the point clouds and the Normal Distributions Transform (NDT) algorithm is usually utilized in the point clouds registration. In this paper, with the aim to reduce the accumulated errors, we proposed a weighted NDT combined with a Local Feature Adjustment (LFA) to process the point clouds and improve the accuracy. Cells of the NDT are weighted according to the range’s values and their surface characteristics, the new cost functions with weight are constructed. In the experiments, we tested NDT-LOAM on the KITTI odometry dataset and compared it with the state-of-the-art algorithm ALOAM/LOAM. NDT-LOAM had 0.899% average drift in translation, better than ALOAM and at the level of LOAM; moreover, NDT-LOAM can run at 10 Hz in real-time, while LOAM runs at 1 Hz. The results display that NDT-LOAM is a real-time and low-drift method with high accuracy. In addition, the source code is uploaded to GitHub and the download link is https://github.com/BurryChen/lv_slam .

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.

With the advancement of LiDAR technology in recent years, various new types of LiDAR have emerged, and the price of LiDAR equipment has gradually decreased. At present, low-cost solid-state LiDAR are gradually occupying the market. To evaluate the performance of two LIDARs for simultaneous localization and mapping. This study investigated the application of solid-state LiDAR and mechanical LiDAR in localization and mapping systems and comparatively analyzed their advantages and disadvantages. We selected some classic open source algorithms (such as A-LOAM) to evaluate the performance of mechanical LiDAR and solid-state LiDAR in localization. The experimental data are adopted from some representative open source data (such as KITTI data) and real data collected by Shenzhen University. The results showed that the localization accuracy of solid-state LiDAR was lower than that of mechanical LiDAR when the mobile robot moved to the corner and faced square to the wall at close range. Moreover, the localization accuracy of solid-state LiDAR was the same as or even higher than that of mechanical LiDAR when the mobile robots had small changes in the field of view and the mobile robot moved along straight lines or other tracks.

A practical back-end module with loop closure detection is very useful and important for a LiDAR simultaneous localization and mapping (SLAM) system to perform high-precision positioning and mapping tasks. However, most existing loop closure detection methods are based on images or point clouds, and these methods may produce errors when the structure or texture is similar. To overcome this problem, we propose a complete LiDAR SLAM system, including a front-end odometry module based on NDT-LOAM and a back-end optimization module with loop closure based on activity semantics. Through the analysis and calculation of inertial measurement unit data from SLAM platforms such as unmanned ground vehicles, the activity semantics of turning and passing over a speed bump are detected based on the peak z-axis angular velocity and z-axis acceleration, respectively. Then, according to this activity semantics information and its unique and definite attributes, we establish correct loop closure detection using rough geometric detection, activity semantics matching, and point cloud rematching for validation. Finally, graph optimization theory is utilized to reduce the global cumulative error, improve the global trajectory accuracy and map consistency, and obtain the final global motion trajectory and point cloud map. We collected a dataset for evaluation, which contains indoor data, outdoor data and indoor-outdoor integration data, and we also evaluated our method on the KITTI dataset. The experimental results for different scenes show that the addition of activity semantics can effectively help loop closure detection and improve LiDAR SLAM system performance.

In the fields of autonomous vehicles, virtual reality and three-dimensional (3D) reconstruction, 2D laser rangefinders have been widely employed for different purposes, such as localization, mapping, and simultaneous location and mapping. However, the extrinsic calibration of multiple 2D laser rangefinders is a fundamental prerequisite for guaranteeing their performance. In contrast to existing calibration methods that rely on manual procedures or suffer from low accuracy, an automatic and high-accuracy solution is proposed in this paper for the extrinsic calibration of 2D laser rangefinders. In the proposed method, a mobile sphere is used as a calibration target, thereby allowing the automatic extrapolation of a spherical center and the automatic matching of corresponding points. Based on the error analysis, a matching machine of corresponding points with a low error is established with the restriction constraint of the scan circle radius, thereby achieving the goal of high-accuracy calibration. Experiments using the Hokuyo UTM-30LX sensor show that the method can increase the extrinsic orientation accuracy to a sensor intrinsic accuracy of 10 mm without requiring manual measurements or manual correspondence among sensor data. Therefore, the calibration method in this paper is automatic, highly accurate, and highly effective, and it meets the requirements of practical applications.

Pedestrian navigation using smartphone built-in sensors attracted wide attention with the booming Location-based Service (LBS). Pedestrian Dead Reckoning (PDR) and Global Navigation Satellite Navigation (GNSS) integration is recognized as a reliable solution for smartphone-based pedestrian navigation. However, GNSS is vulnerable under some conditions. Multi-path, None-Line-Of-Sight (NLOS), and signal blockage all pose negative impacts on GNSS position accuracy. PDR position errors increase with the pedestrian walking distance without GNSS. Aiming at improving the smartphone-based pedestrian position accuracy under GNSS signal challenging conditions, in this paper, we propose a cooperative PDR/GNSS integration method with Factor Graph Optimization (FGO). A factor graph is constructed to represent the relationship between the multiple agents’ states, measurements and inter-ranging information. Optimal estimation is realized considering all the historical measurements and inter-ranging measurements between these agents. Field tests were carried out to assess the performance of the proposed cooperative navigation method. Results manifest that the proposed method can improve the position accuracy especially under the GNSS signals challenging conditions.