Pole-Like Road Object Detection from Mobile Lidar System Using a Coarse-to-Fine Approach

Tee-Ann Teo, Chi Min Chiu

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

The requirements of three-dimensional (3-D) road objects have increased for various applications, such as geographic information systems and intelligent transportation systems. The use of mobile lidar systems (MLSs) running along road corridors is an effective way to collect accurate road inventories, but MLS feature extraction is challenged by the blind scanning characteristics of lidar systems and the huge amount of data involved; therefore, an automatic process for MLS data is required to improve efficiency of feature extraction. This study developed a coarse-to-fine approach for the extraction of pole-like road objects from MLS data. The major work consists of data preprocessing, coarse-to-fine segmentation, and detection. In data preprocessing, points from different trajectories were reorganized into road parts, and building facades alongside road corridors were removed to reduce their influence. Then, a coarse-to-fine computational framework for the detection of pole-like objects that segments point clouds was proposed. The results show that the pole-like object detection rate for the proposed method was about 90%, and the proposed coarse-to-fine framework was more efficient than the single-scale framework. These results indicate that the proposed method can be used to effectively extract pole-like road objects from MLS data.

Original languageEnglish
Article number7229247
Pages (from-to)4805-4818
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume8
Issue number10
DOIs
StatePublished - 1 Oct 2015

Keywords

  • Coarse-to-fine
  • mobile lidar system (MLS)
  • pole-like object extraction

Fingerprint Dive into the research topics of 'Pole-Like Road Object Detection from Mobile Lidar System Using a Coarse-to-Fine Approach'. Together they form a unique fingerprint.

Cite this