[논문]ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building

Lim, Hyungtae; Hwang, Sungwon; Myung, Hyun

doi:10.1109/lra.2021.3061363

[해외논문] ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building 원문보기

IEEE robotics and automation letters, v.6 no.2, 2021년, pp.2272 - 2279

Lim, Hyungtae (KI-AI, KI-R at KAIST (Korea Advanced Institute of Science and Technology), School of Electrical Engineering, Daejeon, South Korea) , Hwang, Sungwon (Robotics Program at KAIST, Daejeon, South Korea) , Myung, Hyun (KI-AI, KI-R at KAIST (Korea Advanced Institute of Science and Technology), School of Electrical Engineering, Daejeon, South Korea)

Abstract ▼ AI-Helper

Scan data of urban environments often include representations of dynamic objects, such as vehicles, pedestrians, and so forth. However, when it comes to constructing a 3D point cloud map with sequential accumulations of the scan data, the dynamic objects often leave unwanted traces in the map. These traces of dynamic objects act as obstacles and thus impede mobile vehicles from achieving good localization and navigation performances. To tackle the problem, this letter presents a novel static map building method called ERASOR, Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal, which is fast and robust to motion ambiguity. Our approach directs its attention to the nature of most dynamic objects in urban environments being inevitably in contact with the ground. Accordingly, we propose the novel concept called pseudo occupancy to express the occupancy of unit space and then discriminate spaces of varying occupancy. Finally, Region-wise Ground Plane Fitting (R-GPF) is adopted to distinguish static points from dynamic points within the candidate bins that potentially contain dynamic points. As experimentally verified on SemanticKITTI, our proposed method yields promising performance against state-of-the-art methods overcoming the limitations of existing ray tracing-based and visibility-based methods.

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