Stereo camera visual odometry for moving urban environments

This work proposes a system designed to estimate the ego-motion of a synchronized calibrated stereo camera in scenes containing a moderate number of moving objects. This is particularly useful in busy road scenes and populated urban areas. The key novelty of the proposed approach is that it estimates the motion of clusters of pixels between stereo frames, which allows it to explicitly reject clusters in motion. This is in contrast to current state-of-the-art algorithms, that tend to treat moving elements as outliers, which are removed using strategies such as RANSAC or M-estimators. Unfortunately treating moving pixels as outliers can give poor performance when the motion represents a significant portion of pixels. The proposed approach overcomes this, if the motion is due to many independently moving objects (such as people or cars). Our experiments show promising results in a variety of urban environments.