Novel robotic mapping system for freshwater environments

Abstract. This paper presents an unmanned surface vehicle (USV) equipped with a mapping system designed to map boreal freshwater environments. The proposed system fuses satellite navigation, inertial measurements, and lidar data to provide accurate and precise three-dimensional (3D) point clouds from the environment around the USV’s path. In order to achieve the required accuracy, we present several calibration methods used including a novel cost function for optimizing a rotation between lidar and inertial frames based on accelerometer measurements and point cloud registration. In the proposed positioning method, a post-processed high-end satellite navigation and inertial fusion trajectory is used as an initial guess of the USV’s pose and for motion compensating lidar data. Pose graph based simultaneous localization and mapping (SLAM) algorithm is used to further refine the map and trajectory using normal distributions transform (NDT) distribution to distribution variant to compute lidar odometry and loop-closures offline after data collection. A method for rating loop-closures is adopted to select scan registration results to add into the pose graph. A factor graph is built using lidar odometry, detected loop-closures, and fused satellite navigation and inertial solution to optimize and solve the optimal trajectory. The conducted experiment demonstrates that the proposed graph-SLAM method significantly improves the overall consistency of the resulting 3D point cloud and the absolute trajectory error (ATE) of the optimized trajectory.