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

TL;DR

This paper introduces TCM-ICP, a novel registration algorithm for multiple LiDAR scans that improves accuracy and robustness over traditional ICP, especially in the presence of outliers, by using a new geometric metric called Transformation Compatibility Measure.

Contribution

The paper proposes TCM-ICP, a new registration method that incorporates a geometric metric to select similar point clouds and employs optimization techniques for improved accuracy.

Findings

Achieves comparable or superior registration accuracy to traditional methods.

Performs well even with outliers in the data.

Validated on four real-world scenes.

Abstract

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.