Camera Tracking in Lighting Adaptable Maps of Indoor Environments

Caselitz, Tim; Krawez, Michael; Sundram, Jugesh; Loock, Mark Van; Burgard, Wolfram

doi:10.1109/icra40945.2020.9197471

Cited by 4 publications

(1 citation statement)

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“…Instead having the robot re-mapping multiple times the environment, a digital twin of the target environment could be created to simulate illumination variations. In Caselitz et al (2020 ), all possible lighting variations (based on combinations of lamps that can be on or off) including shadows are simulated in real time using the latest ray tracing technology. The camera can then be robustly tracked in the environment even if lights are turned off or on (creating drastic changes of illumination) during re-localization.…”

Section: Discussionmentioning

confidence: 99%

Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments

Labbé

Michaud

2022

Front. Robot. AI

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For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY, and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 min intervals during sunset using a Google Tango phone in a real apartment.

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Section: Discussionmentioning

confidence: 99%