NAVER LABS

Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper covariance estimation is thus critical to balance confi- dence levels among sensors. Despite its importance, traditional covariance approximation mostly relied on first order derivative or fixed measurement covariance and therefore tended to be error-prone, and even heuristic. Recently, deep learning for uncertainty estimation yielded meaningful performance, but applied to a single sensor in a supervised manner. Unlike the traditional supervised manner, we introduce an unsupervised loss for uncertainty modeling, to learn uncertainty without needing ground truth covariance as a label. Most important, we overcome the limitation of learning a single sensor’s uncertainty by introducing a way of balancing uncertainty between different sensor modalities. In doing so, we alleviate the uncertainty balancing issue between sensors that has often been encountered in the multi-sensor SLAM application. Targeting covariance learning for visual odometry, particularly with regard to the integration of inertial sensors, the proposed uncertainty learning method was validated in a visual-inertial odometry application over the public datasets under artificial visual and inertial degradations to mimic harsh environment.