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