DNN-based anomaly prediction for the uncertainty in visual SLAM

The method described in this paper proposes a supervised Deep Neural Network (DNN) approach for the prediction of anomalies in camera-based navigation. The method is inspired by the unsolved issues of Integrity Monitors (IMs) when some of the sensor measurement covariances are unknown or inconsistent. Especially, the focus is on predicting when the estimation error distribution would require fatter tails to include outliers. The developed method takes into account single-frame image features as well as transient changes in the error. In the best of our knowledge, this is the first work that predicts anomalies in the error covariance of Simultaneous Navigation and Mapping (SLAM) estimates and associates them with low-level image features. Finally, the prediction method can be used with other sensors as well, allowing the future development of navigation algorithm- and sensor-agnostic safety monitoring frameworks.