Loop closure can effectively eliminate the accumulated error and plays an important role in Simultaneous Localization and Mapping (SLAM). There remains challenges in loop detection and loop correction due to the large viewpoints difference and the environment appearance changes. In this letter, we propose a novel loop closure method based on object modeling and semantic graph matching. We use voxels and cuboids to model the object-level features in the environment, and further represent the environment as a semantic graph with topological information. On this basis, an efficient graph matching method based on edit distance is proposed for robust place recognition. Finally, the loop correction is carried out by object alignment between semantic maps. Experimental results demonstrate that the proposed method can realize omni-directional loop closure without viewpoint constraint, and is robust to environmental appearance changes. Compared with the existing appearance-based methods, the performance has been greatly improved.

Distribution-to-distribution-based lidar odometry is known for its good accuracy, while it cannot run in real-time when the number of points is large. To alleviate this problem, Faster Generalized Iterative Closest Point (FasterGICP) is proposed in this letter, in which an acceptance-rejection sampling-based two-step point filter is proposed to exclude the points that rarely benefit the lidar odometry performance. Specifically, the lidar point cloud is firstly filtered and only the points with high planarity tend to be preserved, which can reduce the distribution approximation errors when the GICP works as a plane-to-plane Iterative Closest Point (ICP). Secondly, during the pose estimation optimization process, the lidar points are further iteratively filtered according to their contributions to the optimization objective function, in which the point's matching error defines the contribution. The two-step filtering process is achieved by designing the target and proposal distributions in the acceptance-rejection sampling framework. With the help of the point filter, our odometry can work in a scan-to-model strategy while demonstrating both efficiency and accuracy improvements. Extensive validation experiments are conducted on the public and our datasets. The results demonstrate that our method can achieve competitive performance compared with the state-of-the-art lidar odometry and Simultaneously Localization and Mapping (SLAM) methods. Our code has been made public available at https://github.com/SLAMWang/fasterGICP .

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

Objects in environments are more stable than feature points, which can benefit for building a wide range of common view relationship and effectively reduce the cumulative error of visual localization. In this paper, we propose Contour-SLAM, the first RGB-D object-level SLAM method that utilizes the projection constraints between the dense object model and its instance masks for state estimation. Firstly, considering that the unstructured object models are difficult to describe mathematically and their re-projection errors are difficult to quantify, we use voxels and cuboids to model objects and define the re-projection error between the 3D object model and its 2D instance segmentation mask based on the outer contour distance. Secondly, we construct a covisibility graph between keyframes based on the shared observations of same feature points and objects, which is further used to maintain a local map including keyframes, map points and object models in real-time. Finally, a multi-view bundle adjustment formulation is proposed to jointly optimize the three components of the local map. Experimental results on public dataset as well as our own collected sequences demonstrate that object modeling and camera pose estimation can benefit each other. The proposed method demonstrates competitive performance compared with the state-of-the-art SLAM methods.

Accurate trajectory prediction of vehicles is essential for reliable autonomous driving. To maintain consistent performance as a vehicle driving around different cities, it is crucial to adapt to changing traffic circumstances and achieve lifelong trajectory prediction model. To realize it, catastrophic forgetting is a main problem to be addressed. In this paper, a divergence measurement method based on conditional Kullback-Leibler divergence is proposed first to evaluate spatiotemporal dependency difference among varied driving circumstances. Then based on generative replay, a novel lifelong vehicle trajectory prediction framework is developed. The framework consists of a conditional generation model and a vehicle trajectory prediction model. The conditional generation model is a generative adversarial network conditioned on position configuration of vehicles. After learning and merging trajectory distribution of vehicles across different cities, the generation model replays trajectories with prior samplings as inputs, which alleviates catastrophic forgetting. The vehicle trajectory prediction model is trained by the replayed trajectories and achieves consistent prediction performance on visited cities. A lifelong experiment setup is established on four open datasets including five tasks. Spatiotemporal dependency divergence is calculated for different tasks. Even though these divergence, the proposed framework exhibits lifelong learning ability and achieves consistent performance on all tasks.

In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception is the basis of intelligent planning and safe decision-making for intelligent vehicles, this paper presents a survey of the existing perceptual methods in vehicles, especially 3D object detection, which guarantees the reliability and safety of vehicles. In this review, we first introduce the role of perceptual module in autonomous driving system and a relationship with other modules. Then, we classify and analyze the corresponding perception methods based on the different sensors. Finally, we compare the performance of the surveyed works on public datasets and discuss the possible future research interests.