Benchmarking Pedestrian Odometry: the Brown Pedestrian Odometry Dataset (BPOD)

This paper presents the Brown Pedestrian Odometry Dataset (BPOD) for benchmarking visual odometry algorithms on data from head-mounted sensors. This dataset was captured with stereo and RGB streams from RealSense cameras with rolling and global shutters in 12 diverse indoor and outdoor locations on Brown University's campus. Its associated ground-truth trajectories were generatedfrom third-person videos that documented the recorded pedestrians' positions relative to stick-on markers placed along their paths. We evaluate the performance of canonical approaches representative of direct, feature-based, and learning-based visual odometry methods on BPOD. Our finding is that current methods which are successful on other benchmarks fail on BPOD. The failure modes correspond in part to rapid pedestrian rotation, erratic body movements, etc. We hope this dataset will play a significant role in the identification of these failure modes and in the design, development, and evaluation of pedestrian odometry algorithms.