State-of-the-art visual odometry algorithms achieve remarkable efficiency and accuracy. Under realistic conditions, however, tracking failures are inevitable and to continue tracking, a recovery strategy is required.
At ICIP 2013, we present a relocalization system that enables realtime, 6D pose recovery for wide baselines. Our approach targets specifically resource-constrained hardware such as mobile phones. By exploiting the properties of low-complexity binary feature descriptors, nearest-neighbor search is performed efficiently using Locality Sensitive Hashing.
Our method does not require time-consuming offline training of hash tables and it can be applied to any visual odometry system. We provide a thorough evaluation of effectiveness, robustness and runtime on an indoor test sequence with available ground truth poses. We investigate the system parameterization and compare the relocalization performance for the three binary descriptors BRIEF, unscaled BRIEF and ORB.
In contrast to previous work on mobile visual odometry, we are able to quickly recover from tracking failures within maps with thousands of 3D feature points.
For more information, please take a look at our ICIP 2013 paper.