Geometric particle swarm optimization for robust visual ego-motion estimation via particle filtering
Conventional particle filtering-based visual ego-motion estimation or visual odometry often suffers from large local linearization errors in the case of abrupt camera motion. The main contribution of this paper is to present a novel particle filtering-based visual ego-motion estimation algorithm that is especially robust to the abrupt camera motion. The robustness to the abrupt camera motion is achieved by multi-layered importance sampling via particle swarm optimization (PSO), which iteratively moves particles to higher likelihood region without local linearization of the measurement equation. Furthermore, we make the proposed visual ego-motion estimation algorithm in real-time by reformulating the conventional vector space PSO algorithm in consideration of the geometry of the special Euclidean group SE(3), which is a Lie group representing the space of 3-D camera poses. The performance of our proposed algorithmis experimentally evaluated and comparedwith the local linearization and unscented particle filter-based visual ego-motion estimation algorithms on both simulated and real data sets.
PaperImage Vis. Comput. (2013) : http://dx.doi.org/10.1016/j.imavis.2013.04.004.
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