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Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

To localize the position and perfect autonomous navigation, building up a map is essential for mobile robots. The map becomes very important when the weather is not appropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors with emotional accuracy. The loop-closure detection is the process through which a robot can acknowledge the location visited previously, which can identify the ultimate solution to the previous problem. The robot faced difficulty identifying its previously visited path when the environment underwent an extreme change. The main motive of our work is to promote a model capable of understanding the scenes that are presented robustly. Moreover, during seasonal changes, this model provides an appropriate loop-closure detection result. Independent component analysis (ICA) and auto-encoder are proposed to complete our research work. ICA is a powerful tool to describe invariant images perfectly. Especially, when the robot moves through a changing environment, ICA provided more accurate outcomes than the other algorithm (baseline algorithm). On the other hand, the auto-encoder can distinguish between two features of scene variant condition and invariant condition. The encoder takes our work’s next steps by discovering possible routes. To analyze the performance, this work uses the baseline method with a precision-recall curve and a fraction of correct matches. The proposed algorithm ICA showed a 91.05% accuracy rate, which is better than the baseline algorithms, and the appropriate route-finding rate using an auto-encoder is also acceptable.