Lifelong Localization in Semi-Dynamic Environment

Mapping and localization in non-static environments are fundamental problems in robotics. Most of previous methods mainly focus on static and highly dynamic objects in the environment, which may suffer from localization failure in semi-dynamic scenarios without considering objects with lower dynamics, such as parked cars and stopped pedestrians. In this paper, we introduce semantic mapping and lifelong localization approaches to recognize semi-dynamic objects in non-static environments. We also propose a generic framework that can integrate mainstream object detection algorithms with mapping and localization algorithms. The mapping method combines an object detection algorithm and a SLAM algorithm to detect semi-dynamic objects and constructs a semantic map that only contains semi-dynamic objects in the environment. During navigation, the localization method can classify observation corresponding to static and non-static objects respectively and evaluate whether those semi-dynamic objects have moved, to reduce the weight of invalid observation and localization fluctuation. Real-world experiments show that the proposed method can improve the localization accuracy of mobile robots in non-static scenarios.

Automated summary

This paper introduces semantic mapping and lifelong localization approaches for recognizing semi-dynamic objects in non-static environments. A generic framework that integrates mainstream object detection algorithms with mapping and localization algorithms is proposed. Real-world experiments demonstrate that the proposed method can improve the localization accuracy of mobile robots in non-static scenarios.