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Multiple Layers of Contrasted Images for Robust Feature-Based Visual Tracking
Ist Teil von
2018 25th IEEE International Conference on Image Processing (ICIP), 2018, p.241-245
Ort / Verlag
IEEE
Erscheinungsjahr
2018
Quelle
IEL
Beschreibungen/Notizen
Feature-based SLAM (Simultaneous Localization and Mapping) techniques rely on low-level contrast information extracted from images to detect and track keypoints. This process is known to be sensitive to changes in illumination of the environment that can lead to tracking failures. This paper proposes a multi-layered image representation (MLI) that computes and stores different contrast-enhanced versions of an original image. Keypoint detection is performed on each layer, yielding better robustness to light changes. An optimization technique is also proposed to compute the best contrast enhancements to apply in each layer. Results demonstrate the benefits of MLI when using the main keypoint detectors from ORB, SIFT or SURF, and shows significant improvement in SLAM robustness.