Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

Martínez, Jorge L.; Morán, Mariano; Morales, José; Reina, Antonio J.; Zafra, Manuel

doi:10.3390/app8030397

Cited by 10 publications

(26 citation statements)

References 29 publications

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“…This section overviews how synthetic 3D laser scans of the mobile robot Andabata on natural environments can be labelled automatically [15]. Andabata is a ground vehicle for outdoor navigation, which is 0.67 m long, 0.54 m wide and 0.81 m high [23]. This skid-steered robot carries a 3D laser rangefinder on top and centered (see Figure 1), which is based on the unrestrained rotation of a commercial two-dimensional (2D) laser scanner around its optical centre [25].…”

Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

“…The horizontal resolution of the 3D rangefinder depends on the turns performed by the entire 2D sensor and its turning speed. Once mounted on Andabata, the blind region of the 3D sensor is a cone that begins at its optical centre (0.723 m above the ground) and encompasses all the vehicle below [23].…”

Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

“…The ranges are employed to calculate the 3D Cartesian coordinates of detected objects. Moreover, by taking into account the pitch and roll angles on the terrain [15], the whole point cloud is levelled to operate as Andabata does [23]. Besides, the intensity measurements are used to label each 3D point distinctively by assigning different reflectivity values to each natural or artificial element.…”

Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

“…To obtain natural-terrain traversability classifiers for 3D point clouds acquired by the mobile robot Andabata [23] on non-slippery solid ground, this paper develops the following main contributions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

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Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

Section: Traversability-labeling Of 3d Laser Scansmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In recent years, the simultaneous localization and mapping (SLAM) based on a 3D LIDAR sensor have become an important topic in robotics due to the rise of autonomous driving technology. Meanwhile, the building of a map has become the basis for an autonomous mobile robot to complete tasks such as inspection and autonomous navigation [1] in some harsh environments.…”

Section: Introductionmentioning

confidence: 99%

MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes

2018

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In large-scale and sparse scenes, such as farmland, orchards, mines, and substations, 3D simultaneous localization and mapping are challenging matters that need to address issues such as maintaining reliable data association for scarce environmental information and reducing the computational complexity of global optimization for large-scale scenes. To solve these problems, a real-time incremental simultaneous localization and mapping algorithm called MIM_SLAM is proposed in this paper. This algorithm is applied in mobile robots to build a map on a non-flat road with a 3D LiDAR sensor. MIM_SLAM's main contribution is that multi-level ICP (Iterative Closest Point) matching is used to solve the data association problem, a Fisher information matrix is used to describe the uncertainty of the estimated pose, and these poses are optimized by the incremental optimization method, which can greatly reduce the computational cost. Then, a map with a high consistency will be established. The proposed algorithm has been evaluated in the real indoor and outdoor scenes as well as two substations and benchmarking dataset from KITTI with the characteristics of sparse and large-scale. Results show that the proposed algorithm has a high mapping accuracy and meets the real-time requirements.

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Object Recognition Using Neural Networks for Robotics Precision Application

Celenta

Guida

2020

Lecture Notes in Mechanical Engineering

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Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

Cited by 10 publications

References 29 publications

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

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

MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes

Object Recognition Using Neural Networks for Robotics Precision Application

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