TCM-ICP: Transformation Compatibility Measure for Registering Multiple LIDAR Scans
This work addresses a fundamental issue in 3D mapping and robotics for applications like urban modeling, but it appears incremental as it builds on existing ICP frameworks with a new metric and optimization tweaks.
The paper tackles the problem of rigid registration for multiple LiDAR scans, which often suffers from local minima and requires coarse initial alignment in traditional ICP methods, by introducing a Transformation Compatibility Measure (TCM) to select similar point clouds and using optimization techniques; experimental results on four real-world scenes show comparable or superior performance to traditional methods, with robustness to outliers.
Rigid registration of multi-view and multi-platform LiDAR scans is a fundamental problem in 3D mapping, robotic navigation, and large-scale urban modeling applications. Data acquisition with LiDAR sensors involves scanning multiple areas from different points of view, thus generating partially overlapping point clouds of the real world scenes. Traditionally, ICP (Iterative Closest Point) algorithm is used to register the acquired point clouds together to form a unique point cloud that captures the scanned real world scene. Conventional ICP faces local minima issues and often needs a coarse initial alignment to converge to the optimum. In this work, we present an algorithm for registering multiple, overlapping LiDAR scans. We introduce a geometric metric called Transformation Compatibility Measure (TCM) which aids in choosing the most similar point clouds for registration in each iteration of the algorithm. The LiDAR scan most similar to the reference LiDAR scan is then transformed using simplex technique. An optimization of the transformation using gradient descent and simulated annealing techniques are then applied to improve the resulting registration. We evaluate the proposed algorithm on four different real world scenes and experimental results shows that the registration performance of the proposed method is comparable or superior to the traditionally used registration methods. Further, the algorithm achieves superior registration results even when dealing with outliers.