Object Recognition Based Robust Mapping for the Autonomous Mobile Robot Under Dynamic Environment

Abstract

Simultaneous Localization and Mapping (SLAM) is an essential technique for autonomous mobile robot. 2D mapping has been greatly developed recently and widely used in navigation tasks since the it is easier to be generated and has fewer computation amount than 3D mapping. However, 2D information is not enough to reflect real 3D environment. This paper proposes a 2D mapping method by using immobile area grid map method. 3D objects are detected by 3D sensor and the object recognition results are reflected to the 2D map by a probability model, in which the object recognition results are used to calculate the degree of using observed information to generate the map. In this way, 3D objects are reflected to the map and potential mobile objects are deleted from the map. The effectiveness of our proposed method is proven by conducting experiments under an indoor environment, where 3D objects (e.g. desks and chairs) and human beings are all contained. With the proposed method, the generated 2D map shows the areas of 3D objects well and deletes the potential moving objects (static human beings) correctly.