Autonomous driving paper index
Vehicle-Mounted Multi-Sensor Fusion Slam System for Autonomous Navigation
One-line summary
The vehicle-mounted multi-sensor fusion simultaneous localization and mapping system (SLAM), aims to solve the problem of accurate positioning and map building for self-driving vehicles in complex environments.
Engineering notes
Key topics: self-driving vehicle, self-driving, bird's eye view, end-to-end, lidar, sensor fusion, multi-sensor fusion. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The vehicle-mounted multi-sensor fusion simultaneous localization and mapping system (SLAM), aims to solve the problem of accurate positioning and map building for self-driving vehicles in complex environments. The system adopts an end-to-end visual odometry design based on the LoFTR matching algorithm, which can provide robust feature extraction and matching performance in dynamic and complex environments. Meanwhile, the system unifies and converts the information from camera, LIDAR, IMU and loop closure detection to the shared space through the bird's eye view fusion method, which combines the advantages of multiple sensors to realize high-precision position estimation and map construction. The experimental results show that the system can accurately match feature points in the circular corridor scene, optimize the feature point layout of the map, and construct a coherent and complete map.
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