HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor
This work addresses lighting robustness in SLAM for robotics and AR applications, but it is incremental as it builds on existing frame-to-model methods with a new exposure-invariant measure.
The paper tackles the problem of robust visual SLAM under varying lighting conditions using a low-cost RGB-D sensor with auto-exposure, resulting in improved tracking accuracy and high dynamic range maps.
We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure. It is based on a normalised radiance measure which is invariant to exposure changes and not only robustifies the tracking under changing lighting conditions, but also enables the following exposure compensation perform accurately to allow online building of high dynamic range (HDR) maps. The latter facilitates the frame-to-model tracking to minimise drift as well as better capturing light variation within the scene. Results from experiments with synthetic and real data demonstrate that the method provides both improved tracking and maps with far greater dynamic range of luminosity.