SLAM – simultaneous localization and mapping – is already a well-established technology in robotics. This generally starts with visual SLAM, using object recognition to detect landmarks and obstacles. VSLAM alone uses a 2D view of a 3D environment, challenging accuracy; improvements depend on complementary sensing inputs such as inertial measurement. VISLAM, as this approach is known, works well in good lighting conditions and does not necessarily depend on fast frame rates for visual recognition. Now automotive applications are adopting SLAM but cannot guarantee good seeing and demand fast response times. LIDAR-based SLAM, aka LOAM – LIDAR Odometry and Mapping – is a key driver in this area.