A terrain description method for traversability analysis based on elevation grid map

Meng, Xiangrui; Cao, Zhiqiang; Liang, Shuang; Pang, Lei; Wang, Shuo; Zhou, Chao

doi:10.1177/1729881417751530

Cited by 16 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, to assess the versatility and safety of a method in different situations, it is crucial to evaluate the segmentation performance with rising obstacles, slopped or rough terrains, and sparse data. Regarding 2.5 Grid-based methods, since the ground is modeled into a grid where each cell represents a small region of the ground plane, almost all of them can perform well with rising obstacles and uneven ground surfaces [ 67 , 68 , 69 , 70 ]. However, they can be unpredictable when dealing with sparse data.…”

Section: Discussionmentioning

confidence: 99%

“…Despite this method allowing for very accurate detection of planar objects, it can wrongly classify objects close to the ground as belonging to the ground plane. To address this issue, Meng et al [ 67 ] apply a height difference kernel over each cell and its neighbors, allowing for detecting cells with a slightly higher average height than neighboring cells. Despite improving the detection of low-height objects, extra steps are necessary to address uneven terrains, which significantly increases the overall algorithm’s complexity.…”

Section: Ground Segmentation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes

Matias

Campos

et al. 2023

Sensors

View full text Add to dashboard Cite

In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Ground Segmentation Methodsmentioning

confidence: 99%

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes

Matias

Campos

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The height difference of the points falling in the same grid cell is then utilized to determine the traversability of that grid cell. In addition to the height difference, the average height (Douillard et al, 2010), the covariance (Hamandi et al, 2018), the slope (Meng et al, 2018), or the roughness (Neuhaus et al, 2009) of the grid cell can all be utilized for traversability analysis. These methods are simple to implement and have low computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Xue

Liang³

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial‐temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real‐time and outperforms state‐of‐the‐art approaches.

show abstract

“…In total, 3 drone images were captured, with average ground sampling distance (GSD) of 3 cm and covering a total area of 1.508 km 2 inside the Seich-Sou forest. DSM data is processed, according to the terrain description method for traversability analysis using an elevation map as introduced by X. Meng et al, 21 in order to create automatic road quality annotation for every segment, in a scale from 1 to 5, with 5 corresponding to streets of excellent condition. This method takes into account height index, roughness, slope angle and can describe different types of terrain, including obstacles, potholes, and slopes.…”

Section: Road Condition Monitoringmentioning

confidence: 99%

A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation

Kelesakis,

Marthoglou,

GRAMMALIDIS

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

View full text Add to dashboard Cite

The role of forest and rural road network is very important and multifaceted for: a) the exploitation of forests/fields and movement of the staff involved in their management and protection, b) access to existing technical facilities, c) tourism and recreation and d) harvesting and transport of products. Hence, it is important to systematically record and monitor the network status, including its road segment status and important Points of Interest. Since in most cases, such detection and monitoring procedures require manual and tedious procedures, the INFOROAD project (https://inforoad.karteco.gr/) (Project code: KMP6-0079153), implemented under the framework of the Action "Investment Plans of Innovation" of the Operational Program "Central Macedonia 2014 2020", that is co-funded by the European Regional Development Fund and Greece", is developing an innovative methodology and tools for automated extraction, mapping and status monitoring of forest and rural road network, by combining remote sensing data with state-of-the-art deep learning approaches. Specifically, automated procedures will be developed for: a) road extraction, b) road network graph detection and c) road segment condition monitoring, from very high-resolution multispectral satellite images using innovative semantic segmentation and classification based on deep learning. From a research perspective, these problems are very challenging, as forest and agricultural roads -as opposed to asphalt roads -exhibit more significant surface and texture variations, while only few related datasets exist. For this reason, additional data, which is necessary for the training and evaluation of the deep learning algorithms, will also be collected, e.g., by UAVs equipped with RGB cameras that can also provide accurate DSM information, manual inspection or other free sources. The associated dataset that will be created for the pilot application of the project in Seich-Sou forest, Thessaloniki, will be made freely available to boost future research on the field. The display and management of this information by relevant stakeholders (forest authorities, public, etc.) will be performed by an online Geographical Information System (WebGIS).

show abstract

A terrain description method for traversability analysis based on elevation grid map

Cited by 16 publications

References 25 publications

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation

Contact Info

Product

Resources

About