Optimization of 3D SLAM and Localization for Quadruped Robot Dogs Using Autoencoder-based Techniques

This paper presents an optimized 3D SLAM and localization framework for quadruped robot dogs using a custom autoencoder-based point cloud compression technique. Our approach combines denoising, voxelization, self-attention, and PointNet modules to reduce the size of the point cloud by more than 80% while removing outliers and maintaining structural clarity. We use a 3D laser-scan matcher for localization, which combines onboard odometry from the Unitree Go2 quadruped robot with LiDAR odometry from a Livox Mid-360 sensor. Our approach maintains accurate pose estimation and considerably reduces processing time by working directly on compressed point clouds. Comparative tests show that the system is appropriate for real-time applications in unstructured contexts due to its enhanced map clarity and decreased localization delay. The proposed framework offers an effective and computationally efficient solution for deploying 3D SLAM in agile legged robots operating in dynamic or hazardous settings.