Vision Based Localization under Dynamic Illumination

Localization in dynamically illuminated environments is often difficult due to static objects casting dynamic shadows. Feature extraction algorithms may detect both the objects and their shadows, producing conflict in localization algorithms. This work examines a colour model that separates brightness from chromaticity and applies it to eliminate features caused by dynamic illumination. The colour model is applied in two novel ways. Firstly, the chromaticity distortion of a single feature is used to determine if the feature is the result of illumination alone i.e. a shadow. Secondly, the chromaticity distortion of features matched between images is examined to determine if the monochrome based algorithm has matched them correctly. These two applications are put through a variety of tests in simulated then real world environments to assess their effectiveness in dynamically illuminated scenarios. The results demonstrate a significant reduction in the number of feature mismatches between images with dynamic light sources. The evaluation of the techniques individually in a Simultaneous Localization and Mapping (SLAM) task show substantial improvements in accuracy, with the combination of the two techniques producing a localization result that is highly robust to the environmental lighting

Improving Robustness of Vision Based Localization Under Dynamic Illumination

A dynamic light source poses significant challenges to vision based localization algorithms. There are a number of real world scenarios where dynamic illumination may be a factor, yet robustness to dynamic lighting is not demonstrated for most existing algorithms. Localization in dynamically illuminated environments is complicated by static objects casting dynamic shadows. Features may be extracted on both the static objects and their shadows, exacerbating localization error. This work investigates the application of a colour model which separates brightness from chromaticity to eliminate features and matches that may be caused by dynamic illumination. The colour model is applied in two novel ways. Firstly, the chromaticity distortion of a single feature is used to determine if the feature is the result of illumination alone. These features are removed before the feature matching process. Secondly, the chromaticity distortion of features matched between images is examined to determine if the monochrome based algorithm has matched them correctly. The evaluation of the techniques in a Simultaneous Localization and Mapping (SLAM) task show substantial improvements in accuracy and robustness