3D point cloud maps are an accumulation of laser scans obtained at different positions and times. Since laser scans represent a snapshot of the surrounding at the time of capture, they often contain moving objects which may not be observed at all times. Dynamic objects in point cloud maps decrease the quality of maps and affect localization accuracy, hence it is important to remove the dynamic objects from 3D point cloud maps. In this paper, we present a robust method to remove dynamic objects from 3D point cloud maps. Given a registered set of 3D point clouds, we build an occupancy map in which the voxels represent the occupancy state of the volume of space over an extended time period. After building the occupancy map, we use it as a filter to remove dynamic points in lidar scans before adding the points to the map. Furthermore, we accelerate the process of building occupancy maps using object detection and a novel voxel traversal method. Once the occupancy map is built, dynamic object removal can run in real-time. Our approach works well on wide urban roads with stopped or moving traffic and the occupancy maps get better with the inclusion of more lidar scans from the same scene.

This paper proposes a robust and reliable motion-based calibration algorithm of multiple LiDARs and INS (in-ertial navigation system). The conventional methods utilize appearance-based matching results or only motions of sensors. Therefore, this paper proposes a graph structure-based optimization method with three special constraints. The first constraint is matching-based constraint. This constraint provides reliable transformations between LiDARs. Second, motion-based constraint allows to calibrate between LiDARs and INS. Last one is rigid body constraint from an assumption of rigid body platform. This constraint significantly improves position errors from ambiguities of monotonous motions, and it makes LiDAR sensors possible to fully calibrate with 6-DoF (degree-of-freedom) motions of INS. The proposed method was analyzed with KITTI data and applied to real autonomous vehicle to show its superiority.

In this paper we present our research work on self-Iocalization of the vehicle (ego-state) aided by visual features and maps. This approach requires only last received GPS location, Odometry estimates as the vehicle moves, a database of visual features around the last received GPS estimate and Standard Definition (SD) Map. Towards this goal, we extract Oriented and Rotated Brief (ORB) Descriptors from the Images collected during a drive and use Bag of Words (BoW) approach for creating a vocabulary of visual words. We also use Inverted File Index method for fast querying of the image descriptor that is seen currently by the ego vehicle against a database of all the descriptors collected for finding the possible locations of the robot. The locations for the corresponding matches are then used to update the measurement model. This approach has helped us to localize within 3 seconds with average position and orientation error of 0.8 m and 0.38 degree respectively for all the sequences.