Real-time automotive street-scene mapping through fusion of improved stereo depth and fast feature detection algorithms
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Javidnia, Hossein, & Corcoran, Peter. (2017). Real-time automotive street-scene mapping through fusion of improved stereo depth and fast feature detection algorithms. Paper presented at the 2017 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 08-10 January.
The real-time tracking of street scenes as a vehicle is driving is a key enabling technology for autonomous vehicles. In this work we provide the basis for such a system through combining an improved advanced random walk with restart technique for stereo depth determination with fast, robust feature detection. The enables tracking and mapping of a wide range of scene structures which can be readily resolved into individual objects and scene elements. Thus it is practical to identify moving objects such as vehicles, pedestrians and fixed objects and structures such as buildings, trees and roadside kerb.