Numpy-only dynamic object removal for LiDAR point clouds — 3D box crop + temporal filtering. No GPU, no deep learning.
LiDAR pose graph optimization with dynamic object removal.
Multimodal perception pipeline using CARLA simulation data for synchronized multicamera RGB, LiDAR, and IMU sensor fusion. Implemented LiDAR-Inertial Odometry and a 15-state ESKF. Generated RGB-colored static-world 3D reconstructions with dynamic object removal, reducing ghost trails.
Offline, containerized M-detector prototype for LiDAR dynamic/static point cloud segmentation.
LiDAR dynamic point cloud filtering with temporal observation, spatial clustering and fusion (ROS Noetic)
Experimental LiDAR inertial SLAM frontend with moving point filtering
Automatic 3D mapping with a Kachaka robot: coverage planning, RGB-D SLAM with live removal of people and moving objects, and 2D/3D alignment.
To associate your repository with the dynamic-object-removal topic, visit your repo's landing page and select "manage topics."