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Abstract-This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives [10], [19]). The method is tested on seven sequences of Velodyne scans acquired in urban environments. Unlike various other standard versions of ICP, which fail to recover correct alignment when the displacement between scans increases, the proposed method is shown to be robust to displacements of several meters. In addition, it is shown to lead to savings in computational times which are potentially critical in real-time applications.

The ability to track moving objects is a key part of autonomous robot operation in real-world environments. Whilst for many tasks knowing the positions of objects may be sufficient, tracking the identity of targets may also be desirable. When objects are well separated preserving identities is trivial, however, the identities of objects that pass close to one another may become confused.This paper considers methods to maintain the identities of tracked objects using a combination of LIDAR and video data. When objects are well separated, they are tracked using location information from the LIDAR. When objects move together and their identities cannot be resolved, interactions are recorded and later resolved using appearance models. A vision based approach is adapted for use with LIDAR data and a new method for identity reasoning is proposed. The methods are validated on a dataset comprising a total of 37906 manually labelled point cloud segments.