Detection and Mapping of Specular Surfaces Using Multibounce Lidar Returns
This addresses a challenge in lidar-based mapping for applications like robotics or autonomous vehicles, where specular surfaces can cause data gaps, though it appears incremental as it builds on existing lidar principles with new derivations.
The paper tackles the problem of detecting and mapping specular surfaces, which are often invisible to conventional lidar, by using multibounce lidar returns to derive expressions for surface geometry retrieval in single-beam and multi-beam flash scenarios, including handling transparent surfaces with mixed reflections.
We propose methods that use specular, multibounce lidar returns to detect and map specular surfaces that might be invisible to conventional lidar systems that rely on direct, single-scatter returns. We derive expressions that relate the time- and angle-of-arrival of these multibounce returns to scattering points on the specular surface, and then use these expressions to formulate techniques for retrieving specular surface geometry when the scene is scanned by a single beam or illuminated with a multi-beam flash. We also consider the special case of transparent specular surfaces, for which surface reflections can be mixed together with light that scatters off of objects lying behind the surface.