Dmitri Kovalenko, Mikhail Korobkin, and Andrey Minin Yandex LLC, Russia
The lidar odometry method, integrating into the computation the knowledge about the physics of the sensor, is proposed. A model of measurement error enables higher precision in estimation of the point normal covariance. Adjacent laser beams are used in a novel outlier correspondence rejection scheme. The method is scored high on KITTI leaderboard with 1.5% positioning error. That of 3.7% is achieved in comparison with the LOAM method on the internal dataset.