Event-based 3D SLAM with a depth-augmented dynamic vision sensor
Robotics and Automation(2014)
Abstract
We present the D-eDVS- a combined event-based 3D sensor - and a novel event-based full-3D simultaneous localization and mapping algorithm which works exclusively with the sparse stream of visual data provided by the D-eDVS. The D-eDVS is a combination of the established PrimeSense RGB-D sensor and a biologically inspired embedded dynamic vision sensor. Dynamic vision sensors only react to dynamic contrast changes and output data in form of a sparse stream of events which represent individual pixel locations. We demonstrate how an event-based dynamic vision sensor can be fused with a classic frame-based RGB-D sensor to produce a sparse stream of depth-augmented 3D points. The advantages of a sparse, event-based stream are a much smaller amount of generated data, thus more efficient resource usage, and a continuous representation of motion allowing lag-free tracking. Our event-based SLAM algorithm is highly efficient and runs 20 times faster than realtime, provides localization updates at several hundred Hertz, and produces excellent results. We compare our method against ground truth from an external tracking system and two state-of-the-art algorithms on a new dataset which we release in combination with this paper.
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Key words
SLAM (robots),image colour analysis,image sensors,D-eDVS-,PrimeSense RGB-D sensor,biologically inspired embedded dynamic vision sensor,continuous representation,depth-augmented 3D point,depth-augmented dynamic vision sensor,dynamic contrast changes,dynamic vision sensors,event-based 3D SLAM,event-based 3D sensor,event-based SLAM algorithm,event-based dynamic vision sensor,event-based full-3D simultaneous localization and mapping algorithm,event-based stream,frame-based RGB-D sensor,generated data,lag-free tracking,localization updates,output data,pixel location,resource usage,sparse stream,tracking system,visual data
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