Towards Dense SLAM with High Dynamic Range Colors

Modern dense visual SLAM systems produce high-fidelity geometric models, yet the quality of their textures lags behind. In part, the problem pertains to naive handling of colors. The RGB triplets from images are averaged straight into the model, ignoring nonlinearity of the image color space, vignetting effects, and variations of exposure time between different frames. In this paper we propose extensions to a surfelbased dense SLAM framework that enable more faithful capture of scene appearance. We adjust the representation to increase the dynamic range of colors, radiometrically rectify images to work in linear color space, and explicitly handle saturated pixels. Differently to the prior work in HDR-aware SLAM, we advocate turning off the automatic exposure function of the camera and incorporate a custom controller in the SLAM loop. We demonstrate improvements in texture quality compared to LDR systems, and show that self-directed exposure time control yields more complete and consistent color models.