Kavli Affiliate: Yi Zhou
| First 5 Authors: Jinghang Li, Bangyan Liao, Xiuyuan LU, Peidong Liu, Shaojie Shen
| Summary:
Predicting a potential collision with leading vehicles is an essential
functionality of any autonomous/assisted driving system. One bottleneck of
existing vision-based solutions is that their updating rate is limited to the
frame rate of standard cameras used. In this paper, we present a novel method
that estimates the time to collision using a neuromorphic event-based camera, a
biologically inspired visual sensor that can sense at exactly the same rate as
scene dynamics. The core of the proposed algorithm consists of a two-step
approach for efficient and accurate geometric model fitting on event data in a
coarse-to-fine manner. The first step is a robust linear solver based on a
novel geometric measurement that overcomes the partial observability of
event-based normal flow. The second step further refines the resulting model
via a spatio-temporal registration process formulated as a nonlinear
optimization problem. Experiments on both synthetic and real data demonstrate
the effectiveness of the proposed method, outperforming other alternative
methods in terms of efficiency and accuracy.
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