Abstract
OctoMap is a popular 3D mapping framework which can model the data consistently and keep the 3D models compact with the octree. However, the occupancy map derived by OctoMap can be incorrect when the input point clouds are with noisy measurements. Point cloud filters can reduce the noisy data, but it is unreasonable to apply filters in a sparse point cloud. In this paper, we present a k-nearest neighbours (k-NN) based inverse sensor model for occupancy mapping. This method represents the occupancy information of one point with the average distance from the point to its k-NN in the point cloud. The average distances derived by all the points and their corresponding k-NN are assumed to be normally distributed. Our inverse sensor model is presented based on this normal distribution. The proposed approach is able to deal with sparse and noisy point clouds. We implement the model in the OctoMap to carry out experiments in the real environment. The experimental results show that the 3D occupancy map generated by our approach is more reliable than that generated by the inverse sensor model in OctoMap.
Original language | English |
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Title of host publication | Towards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings |
Editors | Kaspar Althoefer, Jelizaveta Konstantinova, Ketao Zhang |
Publisher | Springer Verlag |
Pages | 75-86 |
Number of pages | 12 |
ISBN (Print) | 9783030253318 |
DOIs | |
Publication status | E-pub ahead of print - 17 Jul 2019 |
Event | 20th Towards Autonomous Robotic Systems Conference, TAROS 2019 - London, UK United Kingdom Duration: 3 Jul 2019 → 5 Jul 2019 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11650 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 20th Towards Autonomous Robotic Systems Conference, TAROS 2019 |
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Country/Territory | UK United Kingdom |
City | London |
Period | 3/07/19 → 5/07/19 |
Funding
Acknowledgments. Yu Miao thanks University of Bath grant University Research Studentship Award-Engineering and China Scholarship Council grant No. 201706120022 for financial support.
Keywords
- Inverse sensor model
- K-nearest neighbours
- Occupancy mapping
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science