Online Loop-Closure Detection via Dynamic Sparse Representation

Abstract

Visual loop closure detection is an important problem in visual robot navigation. Successful solutions to visual loop closure detection are based on image matching between the current view and the map images. In order to obtain a solution that is scalable to large environments involving thousands or millions of images, the efficiency of a loop closure detection algorithm is critical. Recently people have proposed to apply $l_{1}$-minimization methods to visual loop closure detection in which the problem is cast as one of obtaining a sparse representation of the current view in terms of map images. The proposed solution, however, is insufficient with a time complexity worse than linear search. In this paper, we present a solution that overcomes the inefficiency by employing dynamic algorithms in $l_{1}$-minimization. Our solution exploits the sequential nature of the loop closure detection problem. As a result, our proposed algorithm is able to obtain a performance that is an order of magnitude more efficient than the existing $l_{1}$-minimization based solution. We evaluate our algorithm on publicly available visual SLAM datasets to establish its accuracy and efficiency.