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In this paper, we present a multi-camera visual odometry (VO) system for anautonomous vehicle. Our system mainly consists of a virtual LiDAR and a posetracker. We use a perspective transformation method to synthesize asurround-view image from undistorted fisheye camera images. With a semanticsegmentation model, the free space can be extracted. The scans of the virtualLiDAR are generated by discretizing the contours of the free space. As for thepose tracker, we propose a visual odometry system fusing both the featurematching and the virtual LiDAR scan matching results. Only those feature pointslocated in the free space area are utilized to ensure the 2D-2D matching forpose estimation. Furthermore, bundle adjustment (BA) is performed to minimizethe feature points reprojection error and scan matching error. We apply oursystem to an autonomous vehicle equipped with four fisheye cameras. The testingscenarios include an outdoor parking lot as well as an indoor garage.Experimental results demonstrate that our system achieves a more robust andaccurate performance comparing with a fisheye camera based monocular visualodometry system.

ViLiVO: Virtual LiDAR-Visual Odometry for an Autonomous Vehicle with a Multi-Camera System