Smuggling Tunnel Mapping using Slide Image Registration

Abstract

There exist a large number of unmapped tunnels across the US-Mexican border used primarily for smuggling, which the US Government desires to map using robots. This thesis presents two novel approaches to generate 3D maps of these tunnels. The both algorithms use a frame invariant point descriptor called the Slide Image that was originally developed for underwater SONAR ring localization. The presented algorithms adapt the Slide Images to larger more complex laser scans. Using the Slide Images generated for each 3D laser scan, the first algorithm determines the coordinate transforms needed to fuse the scans. The second algorithm uses the transform generated by the first algorithm as an initial mapping, which the algorithm fine tunes using an Iterative Closest Point approach. This fusion algorithm is able to provide the fine-tuned accuracy of the Iterative Closest Point technique, while retaining the Slide Image’s insensitivity to local minima. Both algorithms are evaluated using a real smuggling tunnel as well as an office environment. The results are compared with the results generated via the existing Iterative Closest Point algorithm. The first algorithm outperformed the Iterative Closest Point algorithm in the smuggling tunnel environment, but encountered difficulty mapping the intersections in the office environment. The Fusion algorithm clearly outperformed both the Slide Image algorithm and the Iterative Closest Point algorithm in both environments because it avoided the local minima the Iterative Closest Point algorithm selected while retaining the fine grain accuracy not possible with the Slide Image algorithm.