Localization is the basic problem of mobile robot. A new approach for information coding and decoding for robot localization is proposed in this paper. information codes are used as landmarks to provide a global attitude reference. The label and location information is stored in the information code and strategically placed in the operating environment. The mobile robot is equipped with a camera that can read the information code for localization purposes Firstly, the current situation of the coding methods used for robot localization is analyzed, and the exposed problems and defects in application are summarized. In view of these problems, a new code method which is more suitable for robot location is designed. Then, the coding and decoding methods of the design are elaborated in detail. Finally, relevant experiments are designed in combination with practical application scenarios. Compared with the traditional QR code decoding method, the effect is better than the traditional QR code.

Visual place recognition(VPR) is challenging because the places to be recognized are often affected by complex environmental changes. It is important for loop closure detection in SLAM. In recent years, a large range of approaches have been developed to address this challenge. Among these approaches, the use of biological heuristics and structural information have improved the VPR performance. In this paper, we attempt to improve the biological heuristic method MCN, and combine the available but often overlooked structural information intra-set similarity to propose our first algorithm SMCN. Furthermore, we propose a novel temporal filter that considers temporal continuity and combines it with SMCN to get our second algorithm SMCNTF. Evaluation of both algorithms on ten dataset combinations shows that, our best model SMCNTF has a maximum increase of 32% in average precision and at least a 100-fold increase in computing efficiency. Moreover, fewer parameters need to be tuned comparing with MCN. Generally, our algorithm is much better than MCN.