Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

Continuous point cloud stitching provides accurate and rapid environmental information for mapping, which plays an essential role in autonomous vehicles. Many methods related to autonomous vehicles are either inaccurate or time-consuming when they reconstruct large scenes. In this work, a feature matching score-based stitching method to reconstruct environments for autonomous vehicles is proposed. The purpose of the method is to address the problems of stitching quality and time consumption by using the feature match- completeness scores. The feature matching score is obtained by calculating the matching rate within an adjacent image pairs that produces a frame of point cloud. The score maintains the correct points, so fewer points require to be stitched. Then, the corresponding criterion is given to evaluate the performance of stitching candidate point clouds. Finally, the points are fit into planes to form a complete and comprehensive scene. The proposed stitching algorithm was verified in several experiments to demonstrate the efficiency and the accuracy of the overall system.

Environment perception is critical for feasible path planning and safe driving for autonomous vehicles. Perception devices, such as camera, LiDAR (Light Detection and Ranging), IMU (Inertial Measurement Unit), etc., only provide raw sensing data with no identification of vital objects, which is insufficient for autonomous vehicles to perform safe and efficient self-driving operations. This study proposes an improved edge-oriented segmentation-based method to detect the objects from the sensed three-dimensional (3D) point cloud. The improved edge-oriented segmentation-based method consists of three main steps: First, the bounding areas of objects are identified by edge detection and stixel estimation in corresponding two-dimensional (2D) images taken by a stereo camera. Second, 3D sparse point clouds of objects are reconstructed in bounding areas. Finally, the dense point clouds of objects are segmented by matching the 3D sparse point clouds of objects with the whole scene point cloud. After comparison with the existing methods of segmentation, the experimental results demonstrate that the proposed edge-oriented segmentation method improves the precision of 3D point cloud segmentation, and that the objects can be segmented accurately. Meanwhile, the visualization of output data in advanced driving assistance systems (ADAS) can be greatly facilitated due to the decrease in computational time and the decrease in the number of points in the object’s point cloud.

Title: New Method of Path Optimization for Medical Logistics Robots | Keywords: medical logistics robot, multiple steps traveling salesman problem model, improved ant colony optimization (IACO), refilling model | Author: Hui Jin, Qingsong He, Miao He, Fangchao Hu, and Shiqing Lu