When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors nonmaximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77%-13.13% on the minimum Gaussian uncertainty error metric and a reduction of 1.63%-5.23% on the minimum Categorical uncertainty error metric. Code will be released at https://github.com/asharakeh/bayes-od-rc.

This work presents a method for underwater stereo localization and mapping for detailed inspection tasks. The method generates dense, geometrically accurate reconstructions of underwater environments by compensating for image distortions due to refraction. A refractive model of the camera and enclosure is calculated offline using calibration images and produces non-linear epipolar curves for use in stereo matching. An efficient block matching algorithm traverses the precalculated epipolar curves to find pixel correspondences and depths are calculated using pixel ray tracing. Finally the depth maps are used to perform dense simultaneous localization and mapping to generate a 3D model of the environment. The localization and mapping algorithm incorporates refraction corrected ray tracing to improve map quality. The method is shown to improve overall depth map quality over existing methods and to generate high quality 3-D reconstructions.