HDRFusion: HDR SLAM Using a Low-Cost Auto-Exposure RGB-D Sensor

Abstract

Most dense RGB/RGB-D SLAM systems require the brightness of 3-D points observed from different viewpoints to be constant. However, in reality, this assumption is difficult to meet even when the surface is Lambertian and illumination is static. One cause is that most cameras automatically tune exposure to adapt to the wide dynamic range of scene radiance, violating the brightness assumption. We describe a novel system - HDRFusion - which turns this apparent drawback into an advantage by fusing LDR frames into an HDR textured volume using a standard RGB-D sensor with auto-exposure (AE) enabled. The key contribution is the use of a normalised metric for frame alignment which is invariant to changes in exposure time. This enables robust tracking in frame-to-model mode and also compensates the exposure accurately so that HDR texture, free of artefacts, can be generated online. We demonstrate that the tracking robustness and accuracy is greatly improved by the approach and that radiance maps can be generated with far greater dynamic range of scene radiance.