Title
Erasor: Egocentric Ratio Of Pseudo Occupancy-Based Dynamic Object Removal For Static 3d Point Cloud Map Building
Abstract
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.
Year
DOI
Venue
2021
10.1109/LRA.2021.3061363
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Mapping, range sensing
Journal
6
Issue
ISSN
Citations 
2
2377-3766
2
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Hyungtae Lim123.75
Sungwon Hwang220.70
Hyun Myung395.94