Depth estimation and object recognition in dark environments using ATIS

This paper describes a novel approach to the problem of autonomous Robot Navigation in environments having less or no source of illumination. We have aimed at depth estimation and object recognition aspects, using the bio-inspired Dynamic Vision Sensor (DVS) asynchronous time-based image sensor (ATIS) silicon retina. Experiments were conducted in a dark environment using the ATIS camera, coupled with a simple point-like white LED light source mounted on the same. Switching the LED on for a fraction of time in the dark environment produced a diverging ripple of events in the ATIS. We show how this event response can be used to quantify the distance of the planar obstacle from the camera and also to characterize the object for use in object recognition. The ripple effect observed can be attributed to the high temporal resolution of the ATIS retina, the small rise time of the LED and the light intensity profile on the wall. In the initial sections of the paper, we have shown the theoretical basis for the phenomenon observed and then moved on to describe the proof of concept for depth estimation and object recognition. The algorithms can be used in robotic systems mounted with the ATIS and LED for real time depth perception and object recognition.