Metric localization plays a critical role in visionbased navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.
Using the Signed Distance Field (SDF) representation built from stereo vision, Huang et al. REF proposed a monocular camera localization method by increasing the coherence between the indirect local structure and the SDF model.
Metric Monocular Localization Using Signed Distance Fields
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science