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Cameras are a crucial exteroceptive sensor for self-driving cars as they arelow-cost and small, provide appearance information about the environment, andwork in various weather conditions. They can be used for multiple purposes suchas visual navigation and obstacle detection. We can use a surround multi-camerasystem to cover the full 360-degree field-of-view around the car. In this way,we avoid blind spots which can otherwise lead to accidents. To minimize thenumber of cameras needed for surround perception, we utilize fisheye cameras.Consequently, standard vision pipelines for 3D mapping, visual localization,obstacle detection, etc. need to be adapted to take full advantage of theavailability of multiple cameras rather than treat each camera individually. Inaddition, processing of fisheye images has to be supported. In this paper, wedescribe the camera calibration and subsequent processing pipeline formulti-fisheye-camera systems developed as part of the V-Charge project. Thisproject seeks to enable automated valet parking for self-driving cars. Ourpipeline is able to precisely calibrate multi-camera systems, build sparse 3Dmaps for visual navigation, visually localize the car with respect to thesemaps, generate accurate dense maps, as well as detect obstacles based onreal-time depth map extraction.

3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection