A Human-Inspired Semantic SLAM Based on Parking-Slot Number for Autonomous Valet Parking

With the rapid development of automatic driving field, automatic parking has become increasingly concerned commercially. SLAM (Simultaneous Localization and Mapping) as an important technology is applied to autonomous valet parking (AVP) in recent years. However, it is difficult to take full advantage of the data from vision sensors owing to abundant similar elements in the parking lot. In this paper, a semantic SLAM based on slot number in parking lot is proposed. The system recognizes the slot number as semantic markers by a CNN (Convolution Neural Network) and classifies the slot according to the semantic markers. Then a semantic ICP (Iterative Closest Point) algorithm is used for mapping and localization modules. The results of the experiments on our simulation dataset show that this method performs well and has a better accuracy than traditional ICP. This work may have a promotion for autonomous valet parking and be applied to commercial products one day.