Bias Compensation in Visual Odometry
Empirical evidence shows that error growth in visual odometry is biased. A projective bias model is developed and its parameters are estimated offline from trajectories encompassing loops. The model is used online to compensate for bias and thereby significantly reduces error growth. We validate our approach with more than 25 km of stereo data collected in two very different urban environments from a moving vehicle. Our results demonstrate significant reduction in error, typically on the order of 50%, suggesting that our technique has significant applicability to deployed robot systems in GPS denied environments.