10. Robust and Fast Registration for Lidar Odometry and Mapping

Outliers, such as sensor noise, abnormal measurements, or dynamic objects, can damage the overall accuracy of a Simultaneous Localization and Mapping (SLAM) system. Aiming at to improve the performance of Lidar SLAM systems in urban scenes containing a large number of outliers, we propose a real-time, feature-based, and outliers-rejection Lidar SLAM system. By embedding an outlier elimination method based on 4-points congruent sets into a state-of-the-art SLAM framework and further optimizing the traditional single-step registration to coarse-to-fine registration, we can solve the problem of time-consuming, high motion drift, and wrong mapping caused by the current Lidar SLAM systems which cannot effectively detect and eliminate the outliers in surrounding environment.