Interest Point Sampling for Range Data Registration in Visual Odometry
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Accurate registration of 3D data is one of the most challenging problems in a number of Computer Vision applications. Visual Odometry is one such application, which determines the motion, or change in position of a moving rover by registering 3D data captured by an on-board range sensor, in a pairwise manner. The performance of Visual Odometry depends upon two main factors, the first being the quality of 3D data, which itself depends upon the type of sensor being used. The second factor is the robustness of the registration algorithm. Where sensors like stereo cameras and LIDAR scanners have been used in the past to improve the performance of Visual Odometry, the introduction of the Velodyne LIDAR scanner is fairly new and has been less investigated, particularly for odometry applications. This thesis presents and examines a new method for registering 3D point clouds generated by a Velodyne scanner mounted on a moving rover. The method is based on one of the the most widely used registration algorithms called Iterative Closest Point (ICP). The proposed method is divided into two steps. The first step, which is also the main contribution of this work, is the introduction of a new point sampling method, which prudently select points that belong to the regions of greatest geometric variance in the scan. Interest Point (Region) Sampling plays an important role in the performance of ICP by effectively discounting the regions with non-uniform resolution and selecting regions with a high geometric variance and uniform resolution. Second step is to use sampled scan pairs as the input to a new plane-to-plane variant of ICP, known as Generalized ICP. Several experiments have been executed to test the compatibility and robustness of Interest Point Sampling (IPS) for a variety of terrain landscapes. Through these experiments, which include comparisons of variants of ICP and past sampling methods, this work demonstrates that the combination of IPS and GICP results in the least localization error as compared to all other tested method.