Inferring sun direction to improve visual odometry: A deep learning approach

Abstract

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, in which the sun is typically not visible. We leverage recent advances in Bayesian convolutional neural networks (BCNNs) to train and implement a sun detection model (dubbed Sun-BCNN) that infers a 3D sun direction vector from a single RGB image. Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme. We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion. We evaluate our method on 21.6 km of urban driving data from the KITTI odometry benchmark where it achieves a median error of approximately 12° and yields improvements of up to 42% in translational average root mean squared error (ARMSE) and 32% in rotational ARMSE compared with standard visual odometry. We further evaluate our method on an additional 10 km of visual navigation data from the Devon Island Rover Navigation dataset, achieving a median error of less than 8° and yielding similar improvements in estimation error. In addition to reporting on the accuracy of Sun-BCNN and its impact on visual odometry, we analyze the sensitivity of our model to cloud cover, investigate the possibility of model transfer between urban and planetary analogue environments, and examine the impact of different methods for computing the mean and covariance of a norm-constrained vector on the accuracy and consistency of the estimated sun directions. Finally, we release Sun-BCNN as open-source software.