Sequential LiDAR

Sequential LiDAR processing focuses on leveraging the temporal information inherent in consecutive LiDAR scans to improve 3D perception tasks. Current research emphasizes developing efficient algorithms, such as those based on transformer networks and particle filters, to fuse spatial and temporal data for applications like object detection, odometry, and place recognition. These advancements are crucial for enhancing the robustness and accuracy of autonomous driving systems and other robotics applications that rely on real-time 3D scene understanding. The development of open-source implementations and standardized benchmarks is fostering collaboration and accelerating progress in the field.