15 accepted papers
The installation of a multi-layer 3D LiDAR atop the vehicle is a widely adopted hardware configuration for map-matching-based localization in intelligent driving. By offering a comprehensive 360° horizontal Field of View (FoV), this setup aims to achieve precise matching outcomes through the imposit…
Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this…
3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene di…
LiDAR is an essential sensor for intelligent vehicles. Recently, LiDARs used in vehicles produced by different companies have significant differences in their scanning patterns. Some vehicles use mechanical and solid-state (repetitive) LiDARs, while others use prism-based (non-repetitive) LiDARs. Th…
Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization…
Vision-based collision prediction for autonomous driving is a challenging task due to the dynamic movement of vehicles and diverse types of obstacles. Most existing methods rely on object detection algorithms, which only predict predefined collision targets, such as vehicles and pedestrians, and can…
Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection. Most existing methods directly inject the TIR modality into the RGB-based framework or simply ensemble the results of two modalities. This, however, could l…
Point cloud registration (PCR) is a key problem for robotics, autonomous driving, and other applications. Constructing generalizable 3D descriptors and determining whether a 3D descriptor is in the overlapping area are challenging tasks in PCR. Despite the fast evolution of learning-based 3D descrip
Tactical planning is crucial for safe and efficient driving on the highway. However, the problem is complicated by the uncertain intention of surrounding vehicles, as well as observation noise caused by measurement noise and perception errors. Rule-based tactical planning methods are ineffective in…
Point cloud registration is a key problem for robotics, computer vision, and other applications. Previous global registration algorithms are sensitive to noises or partial occlusion, while local registration algorithms are highly dependent on initial angles. To solve these problems, we propose Centr
This letter presents a framework for 3D instance segmentation on point clouds. A 3D convolutional neural network is used as the backbone to generate semantic predictions and instance embeddings simultaneously. In addition to the embedding information, point clouds also provide 3D geometric informati
We present a novel key region extraction method of point cloud, ROI-cloud, for LiDAR odometry and localization with autonomous robots. Traditional methods process massive point cloud data in every region within the field of view. In dense urban environments, however, processing redundant and dynamic…
This paper proposes a hierarchical depthwise graph convolutional neural network (HDGCN) for point cloud semantic segmentation. The main chanllenge for learning on point clouds is to capture local structures or relationships. Graph convolution has the strong ability to extract local shape information…
Pedestrian detection in fish-eye images is always an important problem in advanced driver assistance systems (ADAS). In conventional methods, pedestrian detectors will be trained using fish-eye images. But it is hard to collect and label enough fish-eye images manually. Therefore, a new strategy for…
Point set registration is a long addressed problem in lots of pattern recognition tasks. This paper presents a robust point set registration algorithm based on optimization of distance between two probability distributions. A major problem encountered in the point to point algorithms is the definiti…