  We present the Brown Pedestrian Odometry Dataset (BPOD) for benchmarking
visual odometry algorithms in head-mounted pedestrian settings. This dataset
was captured using synchronized global and rolling shutter stereo cameras in 12
diverse indoor and outdoor locations on Brown University's campus. Compared to
existing datasets, BPOD contains more image blur and self-rotation, which are
common in pedestrian odometry but rare elsewhere. Ground-truth trajectories are
generated from stick-on markers placed along the pedestrian's path, and the
pedestrian's position is documented using a third-person video. We evaluate the
performance of representative direct, feature-based, and learning-based VO
methods on BPOD. Our results show that significant development is needed to
successfully capture pedestrian trajectories. The link to the dataset is here:
\url{https://doi.org/10.26300/c1n7-7p93
