DNN-Based Recognition of Pole-Like Objects in LiDAR Point Clouds

Plachetka, Christopher; Fricke, Jenny; Klingner, Marvin; Fingscheidt, Tim

doi:10.1109/itsc48978.2021.9564759

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Chen et al [6] fuse poles information into a non-linear optimization problem to obtain the vehicle location. Plachetka et al [29] use a deep neural network for pole extraction by learning encodings of the point cloud input. In contrast to the aforementioned approaches, we use a projection-based method and avoid the comparable costly processing of 3D point cloud data.…”

Section: Related Workmentioning

confidence: 99%

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Dong¹,

Chen²,

Särkkä³

et al. 2022

Preprint

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Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learningbased pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other stateof-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.

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Section: Related Workmentioning

confidence: 99%

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Dong¹,

Chen²,

Särkkä³

et al. 2022

Preprint

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“…This paper focuses on pole-like objects (PLOs). Detection, delineation and segmentation of PLOs located in a road environment have great importance in roadway inventory (Chen et al, 2022), high density (HD) map generation (Plachetka et al, 2021), city modelling, urban planning, road infrastructure monitoring (Ha and Chaisomphob, 2020), intelligent transportation (Wang et al, 2021;Nurunnabi et al, 2022), traffic management (Tang et al, 2020;Li &Cheng., 2022), and most highly road safety inspection applications, as well as averting roadside accidents (Cabo et al, 2014;Wang et al, 2021). Image and video data are common to use for PLOs detection (Zhang et al, 2018;Sheweta et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning (DL) approaches have been used in PLOs detection and extraction (Fang et al, 2022;Sheweta et al, 2022). Plachetka et al (2021) developed a PLOs recognition algorithm with an end-to-end deep neural network (DNN)-based approach using high-density LiDAR point clouds. This method learns an optimal feature representation for various, principally generic, classes of poles in an end-to-end fashion.…”

Section: Introductionmentioning

confidence: 99%

Detection and Segmentation of Pole-Like Objects in Mobile Laser Scanning Point Clouds

Nurunnabi,

Sadahiro,

Teferle

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Pole-like object (PLO) detection and segmentation are important in many applications, such as 3D city modelling, urban planning, road assets monitoring, intelligent transportation, road safety, and forest monitoring. Arguably, vehicle-based mobile laser scanning (MLS) is the best on-road data acquisition system, because it is fast, precise and non-invasive. As part of that, laser scanning georeferenced data (i.e., point clouds) provide detailed structural morphology of the scanned objects. However, point clouds are not free from outliers and noise. Critically, many of the object extraction methods that depend on local saliency features (e.g., normals)-based segmentation use principal component analysis (PCA). PCA can provide the local features but struggle to produce robust results in the presence of outliers and noise. To reduce the influence of outliers for saliency features estimation and in segmentation, this paper employs Robust distance-based Diagnostic PCA (RD-PCA) coupled with the well-known DBSCAN clustering algorithm. This study contributes to a better understanding of object detection and segmentation by (i) exploring the problems of local saliency features estimation in the presence of outliers and noise; (ii) understanding problems with PCA and why RD-PCA is important; and (iii) introducing a novel method for PLOs detection and segmentation following a robust segmentation approach. The performance of the new algorithm is demonstrated through MLS data acquired in an urban road setup.

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“…This yields essential visual cues for scene understanding, and thus forms a significant part of environment perception. One could argue that amodal perception should be performed after fusion in the occupancy grid/vector space [10], [11], [12], or in some latent space non-interpretable to humans [13], [14]. This work on amodal perception of simple camera data, however, comes with the clear advantage of (a) being human-readable, and (b) that already during multisensor fusion, the redundancy of multiple estimates of occluded objects may yield powerful confidence information.…”

Section: Introductionmentioning

confidence: 99%

Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline

Breitenstein¹,

Fingscheidt²

2022

Preprint

Self Cite

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Amodal perception terms the ability of humans to imagine the entire shapes of occluded objects. This gives humans an advantage to keep track of everything that is going on, especially in crowded situations. Typical perception functions, however, lack amodal perception abilities and are therefore at a disadvantage in situations with occlusions. Complex urban driving scenarios often experience many different types of occlusions and, therefore, amodal perception for automated vehicles is an important task to investigate. In this paper, we consider the task of amodal semantic segmentation and propose a generic way to generate datasets to train amodal semantic segmentation methods. We use this approach to generate an amodal Cityscapes dataset. Moreover, we propose and evaluate a method as baseline on Amodal Cityscapes, showing its applicability for amodal semantic segmentation in automotive environment perception. We provide the means to re-generate this dataset on github 1 .

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DNN-Based Recognition of Pole-Like Objects in LiDAR Point Clouds

Cited by 12 publications

References 27 publications

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Detection and Segmentation of Pole-Like Objects in Mobile Laser Scanning Point Clouds

Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline

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