TCM-ICP: Transformation Compatibility Measure for Registering Multiple
LIDAR Scans
- URL: http://arxiv.org/abs/2001.01129v2
- Date: Fri, 31 Jan 2020 17:20:47 GMT
- Title: TCM-ICP: Transformation Compatibility Measure for Registering Multiple
LIDAR Scans
- Authors: Aby Thomas, Adarsh Sunilkumar, Shankar Shylesh, Aby Abahai T.,
Subhasree Methirumangalath, Dong Chen and Jiju Peethambaran
- Abstract summary: We present an algorithm for registering multiple, overlapping LiDAR scans.
In this work, we introduce a geometric metric called Transformation Compatibility Measure (TCM) which aids in choosing the most similar point clouds for 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.
- Score: 4.5412347600435465
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
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