Automatic Exposure and Pose Estimation Error

Visual odometry estimates a robot’s pose by analysing images it captured and is integral to autonomous navigation. However, when devices operate outdoors, the large changes in brightness may cause localisation failure. This shortcoming motivates us to find methods that ensure visual odometry can function in high dynamic range environments.To mitigate the negative impact of operating in high dynamic range environments, researchers proposed automatic exposure algorithms to select a camera exposure time that maximises their selected image quality metric. However, direct comparisons between the selected image metric and visual odometry performance is scarce. Many of the proposed algorithms go through multiple steps, making it difficult to determine whether the improved pose estimation accuracy is the result of optimising the metric.In this submission, we generated image sequences in a simulation environment and captured real-world photos at different exposure levels. We estimate trajectories for each data set and compare the resulting errors with the image quality metrics. Our tests showed that none of the selected image quality metrics had a strong correlation with absolute translation error. The findings suggest that the current automatic exposure algorithms have yet to find an image quality metric that truly impacts visual odometry performance. Ongoing research should consider alternative metrics or metric combinations.