ViLiVO: Virtual LiDARVisual Odometry for an Autonomous Vehicle with a MultiCamera System In this paper, we present a multicamera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surroundview image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans of the virtual LiDAR are generated by discretizing the contours of the free space. As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results. Only those feature points located in the free space area are utilized to ensure the 2D2D matching for pose estimation. Furthermore, bundle adjustment (BA) is performed to minimize the feature points reprojection error and scan matching error. We apply our system to an autonomous vehicle equipped with four fisheye cameras. The testing scenarios include an outdoor parking lot as well as an indoor garage. Experimental results demonstrate that our system achieves a more robust and accurate performance comparing with a fisheye camera based monocular visual odometry system.

Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VILSLAM accomplishes this by incorporating tightlycoupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loopclosure corrected 6DOF LiDAR poses in realtime and lcm voxel dense maps near realtime. VILSLAM demonstrates improved accuracy and robustness compared to stateoftheart LiDAR methods.