MSL3D: Pointcloud-based muck pile Segmentation and Localization in Unknown SubT Environments

This article presents MSL3D, a novel framework for pointcloud-based muck pile Segmentation and Localization in unknown Sub-Terranean (Sub-T) environments. The proposed framework is capable of progressively segmenting the muck piles and extracting their location in a global constructed point cloud map, using the autonomy sensor payload of mining or robotic platforms. MSL3D is structured in a two layer novel architecture that relies on the geometric properties of muck piles in underground tunnels, where the first layer extracts a local Volume Of Interest (VOI) proposal area out of the registered point cloud and the second layer is refining the muck pile extraction of each VOI proposal in the global optimized point cloud map. The first layer of MSL3D is extracting local VOIs bounded in the look-ahead surroundings of the platform. More specifically, the ceiling, left and right walls as well as the ground are continuously segmented using progessive RANSAC, searching for inclination in the segmented ground area to keep as the next-best local VOI. Once a local VOI is extracted, it is transmitted to the second layer, where it is converted to the world frame coordinates. In the sequel, a morphological filter is applied, in order to segment ground and nonground points, followed by RANSAC once again to extract the remaining points corresponding to the right and left walls. In this approach, Euclidean clustering is utilized to keep the cluster with the majority of points, which is assumed to belong to the muck pile. The efficacy of the proposed novel scheme was successfully and experimentally validated in real and large scale SubT environments by utilizing a custom-made UAV.