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Localization and mapping are key capabilities for self-driving vehicles. Inthis paper, we build on Kimera and extend it to use multiple cameras as well asexternal (eg wheel) odometry sensors, to obtain accurate and robust odometryestimates in real-world problems. Additionally, we propose an effective schemefor closing loops that circumvents the drawbacks of common alternatives basedon the Perspective-n-Point method and also works with a single monocularcamera. Finally, we develop a method for dense 3D mapping of the free spacethat combines a segmentation network for free-space detection with ahomography-based dense mapping technique. We test our system on photo-realisticsimulations and on several real datasets collected on a car prototype developedby the Ford Motor Company, spanning both indoor and outdoor parking scenarios.Our multi-camera system is shown to outperform state-of-the art open-sourcevisual-inertial-SLAM pipelines (Vins-Fusion, ORB-SLAM3), and exhibits anaverage trajectory error under 1% of the trajectory length across more than 8kmof distance traveled (combined across all datasets). A video showcasing thesystem is available at: youtu.be/H8CpzDpXOI8.

Multi-Camera Visual-Inertial Simultaneous Localization and Mapping for Autonomous Valet Parking