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This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems.We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously learns an compressed representation from the rawinput data in an unsupervisedway.Different with the traditional bag-of-words based methods, the deep network has the ability to learn the complex inner structures in image data, while no longer needs to manually design the visual features. Our approach employs the characteristics of the SDA to solve the loop detection problem. The workflow of training the network, utilizing the features and computing the similarity score is presented. The performance of SDA is evaluated by a comparison study with Fab-map 2.0 using data from open datasets and physical robots. The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems.

We describe a new formulation of appearance-only SLAM suitable for very large scale navigation. The system navigates in the space of appearance, assigning each new observation to either a new or previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000km trajectory, with mean filter update times of 14 ms. The 1,000km experiment is more than an order of magnitude larger than any previously reported result. The scalability of the system is achieved by defining a sparse approximation to the FAB-MAP model suitable for implementation using an inverted index. Our formulation of the problem is fully probabilistic and naturally incorporates robustness against perceptual aliasing. The 1,000km data set comprising almost a terabyte of omni-directional and stereo imagery is available for use, and we hope that it will serve as a benchmark for future systems.