Evidence map›Paper›PMID 32899515›Full record

ArticleSensors (Basel, Switzerland)2020

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain.

Wei Chen, Qianjie Liu, Huosheng Hu, Jun Liu, Shaojie Wang, Qingyuan Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wei ChenDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.
Qianjie LiuDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.
Huosheng HuSchool of Computer Science & Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK.ORCID 0000-0001-5797-1412
Jun LiuDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.
Shaojie WangDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.
Qingyuan ZhuDepartment of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361102, China.ORCID 0000-0001-5521-5023

Funding

Key Project in Science and Technology Plan of Xiamen 3502Z20191019National Natural Science Foundation of China 51575463National Natural Science Foundation of China 51905460
6 · The paper itself

Abstract

Obstacle detection is one of the essential capabilities for autonomous robots operated on unstructured terrain. In this paper, a novel laser-based approach is proposed for obstacle detection by autonomous robots, in which the Sobel operator is deployed in the edge-detection process of 3D laser point clouds. The point clouds of unstructured terrain are filtered by VoxelGrid, and then processed by the Gaussian kernel function to obtain the edge features of obstacles. The Euclidean clustering algorithm is optimized by super-voxel in order to cluster the point clouds of each obstacle. The characteristics of the obstacles are recognized by the Levenberg-Marquardt back-propagation (LM-BP) neural network. The algorithm proposed in this paper is a post-processing algorithm based on the reconstructed point cloud. Experiments are conducted by using both the existing datasets and real unstructured terrain point cloud reconstructed by an all-terrain robot to demonstrate the feasibility and performance of the proposed approach.

Indexed as

3D sensingautonomous robotsGaussian kernel functionlaser point cloudsneural networksobstacle detection

Identifiers

PMID32899515
PMCPMC7570553

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.