Outdoor Self-Localization of a Mobile Robot Using Slow Feature Analysis
We apply slow feature analysis (SFA) to the problem of self-localization with a mobile robot. A similar unsupervised hierarchical model has earlier been shown to extract a virtual rat’s position as slowly varying features by directly processing the raw, high dimensional views captured during a training run. The learned representations encode the robot’s position, are orientation invariant and similar to cells in a rodent’s hippocampus.
Here, we apply the model to virtual reality data and, for the first time, to data captured by a mobile outdoor robot. We extend the model by using an omnidirectional mirror, which allows to change the perceived image statistics for improved orientation invariance. The resulting representations are used for the notoriously difficult task of outdoor localization with mean absolute localization errors below 6%.
KeywordsSelf-Localization SFA Mobile Robot Biomorphic System Omnidirectional Vision Outdoor Environment
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