Robust monocular visual odometry by uncertainty voting

Abstract

GPS by itself is not dependable in urban environments, due to signal reception issues such as multi-path effects or occlusion. Other sensor data is required to keep track of the vehicle in absence of a reliable GPS signal. We propose a new method to use a single on-board consumer-grade camera for vehicle motion estimation. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Experimental results show good accuracy and high reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance.