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3D Laser Odometry for a Mobile Robot Platform

We propose a method for odometry in real-time using a 2D lidar with a pitching motor. The self-position estimation problem is difficult due to the measurement datasets of point clouds collected at different time stamps, and errors in motion estimation leading to the registration problem for distortion removed point cloud. To validate the proposed method, the corresponding 3D maps can be built without the need for inertial measurements. However, using an Inertial Measurement Unit (IMU) may help the motion estimation in the Levenberg-Marquardt iterative process. This paper aim to decompose difficult problem in mobile robot localization and navigation in two parts: One process calculates the robot's motion estimation in a rough way; another method matches the neighbor frame of point clouds obtained in the global reference for the accurate transform matrix. Thus we obtain the odometry of the robot platform, by implementing the precise motion estimation, we can then construct the environment map consequently. The algorithm is tested by dataset from experiments in the factory laboratory, and the reconstruction map show that our implementation is efficient and accurate.