Optimized Contrast Enhancements to Improve Robustness of Visual Tracking in a SLAM Relocalisation Context

Xi Wang 1 Marc Christie 1 Eric Marchand 2
1 MIMETIC - Analysis-Synthesis Approach for Virtual Human Simulation
UR2 - Université de Rennes 2, Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
2 RAINBOW - Sensor-based and interactive robotics
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Robustness of indirect SLAM techniques to light changing conditions remains a central issue in the robotics community. With the change in the illumination of a scene, feature points are either not extracted properly due to low contrasts, or not matched due to large differences in descriptors. In this paper, we propose a multi-layered image representation (MLI) in which each layer holds a contrast enhanced version of the current image in the tracking process in order to improve detection and matching. We show how Mutual Information can be used to compute dynamic contrast enhancements on each layer. We demonstrate how this approach dramatically improves the robustness in dynamic light changing conditions on both synthetic and real environments compared to default ORB-SLAM. This work focalises on the specific case of SLAM relocalisation in which a first pass on a reference video constructs a map, and a second pass with a light changed condition relocalizes the camera in the map.
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Xi Wang, Marc Christie, Eric Marchand. Optimized Contrast Enhancements to Improve Robustness of Visual Tracking in a SLAM Relocalisation Context. IROS'18 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2018, Madrid, Spain. pp.1-6. ⟨hal-01852003⟩

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