3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

Asvadi, Alireza; Premebida, Cristiano; Peixoto, Paulo; Nunes, Urbano

doi:10.1016/j.robot.2016.06.007

Cited by 226 publications

(86 citation statements)

References 24 publications

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“…Three main representations of the points are commonly used, including point clouds, features, and grids [23].…”

Section: Representationmentioning

confidence: 99%

“…As pointed out in [23], this approach is memory-efficient and has no dependency on predefined features. However, it is not straightforward to determine the size of the discretization.…”

Section: Representationmentioning

confidence: 99%

“…However, as mentioned in [23,51], for non-planar surfaces, such as undulated roads, uphill, downhill, and humps, this model fitting method is not adequate.…”

Section: Segmentation Algorithmsmentioning

confidence: 99%

“…Asvadi et al in [51] divided the space in front of the vehicle into several equal-distant (5 m) strips and fit one plane for each strip based on least square fitting. In [23], a piecewise ground surface estimation was proposed, which consist of four steps: slicing, gating, plane fitting, and validation. The slicing step slices the space in front of the vehicle into regions with approximately equal number of LIDAR points, whereas the gating step rejects outliers in each region based on interquartile range method.…”

Section: Segmentation Algorithmsmentioning

confidence: 99%

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Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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“…Three main representations of the points are commonly used, including point clouds, features, and grids [23].…”

Section: Representationmentioning

confidence: 99%

“…As pointed out in [23], this approach is memory-efficient and has no dependency on predefined features. However, it is not straightforward to determine the size of the discretization.…”

Section: Representationmentioning

confidence: 99%

“…However, as mentioned in [23,51], for non-planar surfaces, such as undulated roads, uphill, downhill, and humps, this model fitting method is not adequate.…”

Section: Segmentation Algorithmsmentioning

confidence: 99%

Section: Segmentation Algorithmsmentioning

confidence: 99%

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Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Planar ground assumptions are usually considered to estimate a planar surface parameters. Non planar surfaces can be approximated with several planes as in[3].Planar surface extraction: Extract other dominant planes present in the geometric representation, commonly linked to building facades. 4.…”

mentioning

confidence: 99%

Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps

Kiran

Roldão

Irastorza

et al. 2019

Lecture Notes in Computer Science

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Lidar has become an essential sensor for autonomous driving as it provides reliable depth estimation. Lidar is also the primary sensor used in building 3D maps which can be used even in the case of low-cost systems which do not use Lidar. Computation on Lidar point clouds is intensive as it requires processing of millions of points per second. Additionally there are many subsequent tasks such as clustering, detection, tracking and classification which makes real-time execution challenging. In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing. The prior 3D maps provide a static background model and we formulate dynamic object detection as a background subtraction problem. Computation and modeling challenges in the mapping and online execution pipeline are described. We propose a rejection cascade architecture to subtract road regions and other 3D regions separately. We implemented an initial version of our proposed algorithm and evaluated the accuracy on CARLA simulator.

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From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges

Saranti,

Pfeifer,

Gollob

et al. 2024

WIREs Data Min & Knowl

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We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in graph neural networks (GNNs) and their evolution with explainable artificial intelligence (XAI), and 3D geometric priors with the human‐in‐the‐loop. We follow a simple definition of a “digital twin,” as a high‐precision, three‐dimensional digital representation of a physical object or environment, captured, for example, by Light Detection and Ranging (LiDAR) technology. After a digression into transforming PCD into images, graphs, combinatorial complexes and hypergraphs, we explore recent developments in geometric deep learning (GDL) and provide insight into the application of these network architectures for analyzing and learning from graph‐structured data. We emphasize the importance of the explainability of these models and recognize that the ability to interpret and validate the results of complex models is a crucial aspect of their wider adoption.This article is categorized under: Technologies > Artificial Intelligence

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3D Lidar-based static and moving obstacle detection in driving environments: An approach based on voxels and multi-region ground planes

Cited by 226 publications

References 24 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Real-Time Dynamic Object Detection for Autonomous Driving Using Prior 3D-Maps

From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges

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