σ-DVO: Sensor Noise Model Meets Dense Visual Odometry

In this paper we propose a novel method called s-DVO for dense visual odometry using a probabilistic sensor noise model. In contrast to sparse visual odometry, where camera poses are estimated based on matched visual features, we apply dense visual odometry which makes full use of all pixel information from an RGB-D camera. Previously, t-distribution was used to model photometric and geometric errors in order to reduce the impacts of outliers in the optimization. However, this approach has the limitation that it only uses the error value to determine outliers without considering the physical process. Therefore, we propose to apply a probabilistic sensor noise model to weigh each pixel by propagating linearized uncertainty. Furthermore, we find that the geometric errors are well represented with the sensor noise model, while the photometric errors are not. Finally we propose a hybrid approach which combines t-distribution for photometric errors and a probabilistic sensor noise model for geometric errors. We extend the dense visual odometry and develop a visual SLAM system that incorporates keyframe generation, loop constraint detection and graph optimization. Experimental results with standard benchmark datasets show that our algorithm outperforms previous methods by about a 25% reduction in the absolute trajectory error.