Using Learning of Speed to Stabilize Scale in Monocular Localization and Mapping

摘要

Monocular visual localization and mapping algorithms are able to estimate the environment only up to scale, a degree of freedom which leads to scale drift, difficulty closing loops, and eventual failure. This paper describes an image-driven approach for scale-drift correction which uses a convolutional neural network to infer the speed of the camera from successive monocular video frames. We obtain continuous drift correction, avoiding the need for explicit higher-level representations of the map to resolve scale. We also propose a novel method of including speed estimates as a regularizer in bundle adjustment which avoids the pitfalls of sudden imposition of scale knowledge. We demonstrate our approach using long-distance sequences for which ground truth is available, and find output that is essentially free of scale drift. We compare the performance with number of other methods for scale-drift correction from monocular data, and show that our solution achieves more accurate results.