Abstract

Not all measured features in SLAM/SfM contribute to accurate localization during the estimation process, thus it is sensible to utilize only those that do. This paper describes a method for selecting a subset of features that are of high utility for localization in the SLAM/SfM estimation process. It is derived by examining the observability of SLAM and, being complimentary to the estimation process, it easily integrates into existing SLAM systems. The measure of estimation utility is formulated with temporal and instantaneous observability indices. Effificient computation strategies for the observability indices are described based on incremental singular value decomposition (SVD) and greedy selection for the temporal and instantaneous observability indices, respectively. The greedy selection is near-optimal since the observability index is (approximately) submodular. The proposed method improves localization and data association. Controlled synthetic experiments with ground truth demonstrate the improved localization accuracy, and real-time SLAM experiments demonstrate the improved data association.