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In recent years, there has been a rapid increase in the number of servicerobots deployed for aiding people in their daily activities. Unfortunately,most of these robots require human input for training in order to do tasks inindoor environments. Successful domestic navigation often requires access tosemantic information about the environment, which can be learned without humanguidance. In this paper, we propose a set of DEDUCE - Diverse scEne Detectionmethods in Unseen Challenging Environments algorithms which incorporate deepfusion models derived from scene recognition systems and object detectors. Thefive methods described here have been evaluated on several popular recent imagedatasets, as well as real-world videos acquired through multiple mobileplatforms. The final results show an improvement over the existingstate-of-the-art visual place recognition systems.

DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments