Learning to Bundle-adjust: A Graph Network Approach to Faster Optimization of Bundle Adjustment for Vehicular SLAM

Bundle adjustment (BA) occupies a large portion of the execution time of SfM and visual SLAM. Local BA over the latest several keyframes plays a crucial role in visual SLAM. Its execution time should be sufficiently short for robust tracking; this is especially critical for embedded systems with a limited computational resource. This study proposes a learning-based bundle adjuster using a graph network. It works faster and can be used instead of conventional optimization-based BA. The graph network operates on a graph consisting of the nodes of keyframes and landmarks and the edges representing the landmarks' visibility. The graph network receives the parameters' initial values as inputs and predicts their updates to the optimal values. It internally uses an intermediate representation of inputs which we design inspired by the normal equation of the Levenberg-Marquardt method. It is trained using the sum of reprojection errors as a loss function. The experiments show that the proposed method outputs parameter estimates with slightly inferior accuracy in 1/60-1/10 of time compared with the conventional BA.