Metric Monocular Localization Using Signed Distance Fields

Related Publications

  1. Metric Monocular Localization Using Signed Distance Fields
    Huaiyang Huang, Yuxiang Sun, Haoyang Ye, Ming Liu.
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
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Abstract

Metric localization plays a critical role in vision-based 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.

Demo Videos

Indoor experiment on the EuRoC dataset, sequence MH_01_easy. Dense map is built by Voxblox on MH_02_easy.

Outdoor experiment on the Newer College dataset.