Alignment and 3D scene change detection for segmentation in autonomous earth moving

The tasks of region or object segmentation and environment change detection in a 3D context are investigated and tested on an autonomous skid steer loader. This is achieved through a technique analogous to background subtraction utilising 3D scan data which is first aligned before a voxel sub traction operation against a prior map. We highlight the close relationships between the scan-to-map alignment, background subtraction and 3D scan-to-map matching problems. The presented approaches take advantage of previous work on the multi-resolution occupied voxels list (MROL) representations for 3D spatial maps. This prior work is augmented to provide a mechanism for fast local 6DOF alignment with the same data structures (MROL) that have previously been shown to allow efficient global localisation. The new approach is then compared to an iterative closest point (ICP) implementation and was found to execute in a similar amount of time, but was more robust and accurate. The local alignment algorithm is inherently more amenable to the MROL representation with an associated reduction in implementation complexity and negligible parameter tuning. The hash value basis of MROL results in a map representation that can be both updated and queried in constant time regardless of mapped volume. The approach described was tested on an autonomous skid steer loader as part of the dig-planning process by segmenting piles of material and detecting humans in the scene.