Browse/search for people

Publication - Professor Andrew Calway

    HDRFusion

    HDR SLAM using a low-cost auto-exposure RGB-D sensor

    Citation

    Li, S, Handa, A, Zhang, Y & Calway, A, 2016, ‘HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor’. in: 2016 Fourth International Conference on 3D Vision (3DV 2016): Proceedings of a meeting held 25-28 October 2016, Stanford, CA, USA. Institute of Electrical and Electronics Engineers (IEEE), pp. 314-322

    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 dif- ficult to meet even when the surface is Lambertian and il- lumination is static. One cause is that most cameras auto- matically 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 ap- parent drawback into an advantage by fusing LDR frames into an HDR textured volume using a standard RGB-D sen- sor 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 track- ing robustness and accuracy is greatly improved by the ap- proach and that radiance maps can be generated with far greater dynamic range of scene radiance.

    Full details in the University publications repository