Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Martínez, Jorge L.; Morán, Mariano; Morales, José; Robles, Alfredo; Sánchez, Manuel

doi:10.3390/app10031140

Cited by 18 publications

(16 citation statements)

References 23 publications

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“…Several terrain classification methods have been tested by Martínez et al [ 53 ], ranging from decision trees to random forests. The authors trained all the classifiers on synthetic 3D laser scanner data generated in simulation and tested them on data acquired from the on-board 3D LIDAR of a skid steering vehicle moving in the real environment.…”

Section: Terrain Traversability Analysismentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

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The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

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Section: Terrain Traversability Analysismentioning

confidence: 99%

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Guastella

Muscato

2020

Sensors

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“…Traversability is individually assessed for each Cartesian point with a random-forest classifier from the machine-learning library Scikit-learn [ 37 ]. This estimator was previously trained with synthetic data providing the most accurate results for real data from Andabata among other available classifiers from this freely available library [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

“…Then, traversability is assessed for individual points with a random-forest classifier [ 14 ]. For every scan, a 3D tree data structure is built, and three spatial features for every point are deduced from its five closest neighbors.…”

Section: Reactive-navigation Schemementioning

confidence: 99%

“…Procedures for assessing terrain traversability can be specifically designed [ 9 , 10 ], but they can also be trained with real data [ 11 , 12 ] and by means of synthetic data [ 13 , 14 ]. This relevant analysis is usually performed with three-dimensional (3D) point clouds of the surroundings acquired from an on-board sensor [ 8 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…This paper pursues to enhance our previous work [ 25 ] about unmanned navigation at low speeds with mobile robot Andabata, which carries an actuated 2D laser scanner as its main exteroceptive sensor. To this end, a previously trained classifier was used to analyse point traversability of levelled 3D depth data acquired in motion [ 14 ]. This reliable ground assessment was employed to continuously build local 2D traversability maps for reactive operation.…”

Section: Introductionmentioning

confidence: 99%

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Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability

Martínez

Morales

Sánchez

et al. 2020

Sensors

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Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describes the case study of mobile robot Andabata that classifies traversable points from 3D laser scans acquired in motion of its vicinity to build 2D local traversability maps. Realistic robotic simulations with Gazebo were employed to appropriately adjust reactive behaviors. As a result, successful navigation tests with Andabata using the robot operating system (ROS) were performed on natural environments at low speeds.

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Overview of Terrain Traversability Evaluation for Autonomous Robots

Shu,

Dong,

Liu

et al. 2024

Journal of Field Robotics

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Traversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open‐source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.

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Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Cited by 18 publications

References 23 publications

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Learning-Based Methods of Perception and Navigation for Ground Vehicles in Unstructured Environments: A Review

Reactive Navigation on Natural Environments by Continuous Classification of Ground Traversability

Overview of Terrain Traversability Evaluation for Autonomous Robots

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