Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations

Abstract

Abstract:Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture-such as the pillars of buildings-will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation.

Toward utilizing multitemporal multispectral airborne laser scanning, Sentinel-2, and mobile laser scanning in map updating

“…a new change detection method that uses multitemporal multispectral ALS data from two dates 2. a new continuous change monitoring method that uses a time series of Sentinel-2 satellite images 3. a method for urban scene classification that uses MLS data from one date (following Ref. 64). The experimental change detection results (methods 1 and 2) and classification results (method 3) obtained by using these methods are also presented in this section.…”

“…Our method for classifying MLS data consists of three steps: data preprocessing, ground removal, and classification. 64 In the first phase, we remove noise and partition data into blocks. Then, ground points are removed.…”

Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

“…For pole-like street furniture segmentation, most state-of-the-art methods perform isolation analysis [1,17,18], detect geometric linear features based on confusion matrices [19][20][21], accurately model pole-like street furniture by RANSAC algorithm [10,11] and conduct voxel or supervoxel-based segmentation [1,15,20,[22][23][24][25][26][27][28][29]. Brenner et al first proposed a double-cylinder model to perform isolation analysis, in which the pole-part should be surrounded by an inner cylinder and there should be no points in between the inner and outer cylinders [17].…”

“…Mobile laser-scanning systems can directly collect the surface information of street objects in cities and attain 3D point cloud data that depict the geometric shapes of these objects. Many researchers have investigated object recognition from mobile LiDAR data in a variety of areas, including road surface detection and reconstruction [2,3], road marking detection and classification [3][4][5][6], road trees recognition [7][8][9], and pole-like street furniture extraction and classification [1,[10][11][12][13][14][15][16]. Nowadays, mobile LiDAR systems are acquiring increasing attention in pole-like street furniture element information extraction in road environment.…”

Mobile mapping of night-time road environment lighting conditions

“…An MLS is suitable for convenient measurement of road environments. Mobile laser-scanned point clouds have been used to analyse and extract road environment elements (Jaakkola et al 2008;Lehtomäki et al 2010;Jochem et al 2011;Manandhar and Shibashaki 2002;Yang et al 2012;Wu et al 2013;Puente et al 2013;Cabo et al 2016;Cheng et al 2017;Holgado-Barco et al 2017;Kumar et al 2017;Wang et al 2017;Yang et al 2017;Li et al 2018). Integrating image textures into a 3D point cloud improves the identification of road environment elements.…”