Lidar Inertial

Lidar-inertial systems integrate LiDAR point cloud data with inertial measurement unit (IMU) data for robust and accurate simultaneous localization and mapping (SLAM). Current research emphasizes tightly-coupled fusion methods, often employing optimization techniques like graph-based SLAM or Kalman filtering, and exploring novel approaches such as continuous-time trajectory representation and efficient data structures like voxel hashing to improve real-time performance and scalability. These advancements are driving improvements in autonomous navigation for robots operating in diverse environments, from challenging indoor spaces to hazardous outdoor terrains like glaciers, and are crucial for applications ranging from autonomous driving to environmental monitoring.