Abstract. Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.
|Journal||ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences|
|Publication status||Published - 4 Jun 2018|
|MoE publication type||A4 Article in a conference publication|
|Event||ISPRS TC II Mid-Term Symposium "Towards Photogrammetry 2020" - Riva del Garda, Italy|
Duration: 4 Jun 2018 → 7 Jun 2018