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Rigid registration of multi-view and multi-platform LiDAR scans is afundamental problem in 3D mapping, robotic navigation, and large-scale urbanmodeling applications. Data acquisition with LiDAR sensors involves scanningmultiple areas from different points of view, thus generating partiallyoverlapping point clouds of the real world scenes. Traditionally, ICP(Iterative Closest Point) algorithm is used to register the acquired pointclouds together to form a unique point cloud that captures the scanned realworld scene. Conventional ICP faces local minima issues and often needs acoarse initial alignment to converge to the optimum. In this work, we presentan algorithm for registering multiple, overlapping LiDAR scans. We introduce ageometric metric called Transformation Compatibility Measure (TCM) which aidsin choosing the most similar point clouds for registration in each iteration ofthe algorithm. The LiDAR scan most similar to the reference LiDAR scan is thentransformed using simplex technique. An optimization of the transformationusing gradient descent and simulated annealing techniques are then applied toimprove the resulting registration. We evaluate the proposed algorithm on fourdifferent real world scenes and experimental results shows that theregistration performance of the proposed method is comparable or superior tothe traditionally used registration methods. Further, the algorithm achievessuperior registration results even when dealing with outliers.

TCM-ICP: Transformation Compatibility Measure for Registering Multiple LIDAR Scans