Dynamic adaptive simultaneous localization and mapping technique for scene change

How to make simultaneous localization and mapping (SLAM) system to be robust and reliable in complex application scenarios has been widely regarded as a key part of automatic and practical robots. Robustness of SLAM system is one of the research focuses in SLAM field. To tackle this issue, we present multi-scene SLAM technique in this paper, which is a novel extensible SLAM framework by combing different SLAM systems facilitated by a scene detection method. Firstly, we propose a robust and extensible SLAM framework called SceneSLAM to enhance the self-adaptive performance of current SLAM systems. By model structure design, it is easy to utilize open-source SLAM systems to be SLAM models of SceneSLAM and modify the scene detection models to support complex application scenarios. Based on the scene detection results, it can schedule the SLAM models automatically. Secondly, aiming at indoor, outdoor and dark scene detection problems, we present a scene detection model based on convolutional neural network and Bias filter optimization. Experimental results show that the proposed scene detection model has reliable accuracy and stability in detecting indoor, outdoor and dark scenes. What’s more, considering sensor failure in indoor, outdoor and dark scenes, we design dynamic adaptive SLAM models to handle the issue. Map fusion is performed to achieve globally consistent localization and mapping when SLAM models scheduled. At last but not least, we build up a prototype system based on SceneSLAM to enhance the reliability of existing SLAM systems when the scene changes between indoor, outdoor and dark scenes. This system runs on a TurtleBot robot equipped with a Kinect sensor. The experimental results show that multi-scene SLAM technique proposed in this paper provides an effective solution to enhance the robustness of existing SLAM systems in dynamic environments.