DynamicFilter: an Online Dynamic Objects Removal Framework for Highly Dynamic Environments
Tingxiang Fan, Bowen Shen, Hua Chen, Wei Zhang, Jia Pan
Abstract
Emergence of massive dynamic objects will diversify spatial structures when robots navigate in urban environments. Therefore, the online removal of dynamic objects is critical. In this paper, we introduce a novel online removal framework for highly dynamic urban environments. The framework consists of the scan-to-map front-end and the map-to-map back-end modules. Both the front- and back-ends deeply integrate the visibility-based approach and map-based approach. The experiments validate the framework in highly dynamic simulation scenarios and real-world dataset.
Topics & Concepts
Computer scienceVisibilityRobotArtificial intelligenceFront (military)Computer visionEngineeringGeographyMeteorologyMechanical engineeringRobotics and Sensor-Based LocalizationVideo Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques