An Occupancy Mapping Method Based on K-Nearest Neighbours
Abstract
:1. Introduction
- a k-NN method for occupancy mapping using the context of neighbouring points to update nodes containing points;
- definition of the relationship between the average distance and the change in occupancy probability, potentially decreasing the probability of a node despite the points being present in the node;
- the proposed k-NN method is verified by the point clouds derived by the StereoSGBM algorithm [21] implemented on the images produced from a stereo camera, and can be potentially extended to other point-cloud-based mapping systems.
2. Background
3. Method
3.1. K-NN-Based Inverse Sensor Model
- is the upper clamping threshold, which is the upper bound on the probability.
- is the threshold. A node will be marked as occupied when the threshold is reached.
- is the probability of a “miss”. A node will be updated with if it is traversed by rays and corresponding endpoints are within range .
- is the probability of a “miss”. A node will be updated with if it is traversed by rays and corresponding endpoints are outside range .
- is the lower clamping threshold, which is the lower bound on the probability.
- is the upper bound on the probability derived by the average distance from a point to its k-NN.
- is the lower bound on the probability derived by the average distance from a point to its k-NN.
- k is the number of nearest neighbouring points.
3.2. Distribution of Average Distances
3.3. Map Update
Algorithm 1: Map Update |
3.4. Parameter Space Considerations
3.5. Parameter Reduction and Optimisation
3.6. Run Time
4. Experiments
4.1. Overview of Data Sets
4.2. Map Generation and Node Classification
4.3. Parameter Space for Analysis
4.4. Results
4.5. Discussion
5. Conclusions
- The k-NN model is nonsensitive to different types of distributions.
- Parameter k is of lower impact than other k-NN parameters.
- Through grid search optimisation, the optimal performance of OctoMap can be improved by the k-NN method.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameter | Minimum | Maximum | Step | Method |
---|---|---|---|---|
a | 0.5 | 0.98 | 0.12 | k-NN |
b | 0.98 | 0.98 | N/A | Both |
0.5 | 0.98 | 0.12 | OctoMap | |
0.02 | 0.38 | 0.12 | Both | |
0.02 | 0.38 | 0.12 | k-NN | |
a | 0.02 | 0.38 | 0.12 | k-NN |
b | 0.02 | 0.02 | N/A | Both |
0.12 | Both | |||
0.02 | 0.98 | 0.12 | k-NN | |
0.02 | 0.98 | 0.12 | k-NN | |
ka | 1 | 7 | 2 | k-NN |
kb | 1 | 1 | N/A | k-NN |
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Miao, Y.; Hunter, A.; Georgilas, I. An Occupancy Mapping Method Based on K-Nearest Neighbours. Sensors 2022, 22, 139. https://doi.org/10.3390/s22010139
Miao Y, Hunter A, Georgilas I. An Occupancy Mapping Method Based on K-Nearest Neighbours. Sensors. 2022; 22(1):139. https://doi.org/10.3390/s22010139
Chicago/Turabian StyleMiao, Yu, Alan Hunter, and Ioannis Georgilas. 2022. "An Occupancy Mapping Method Based on K-Nearest Neighbours" Sensors 22, no. 1: 139. https://doi.org/10.3390/s22010139
APA StyleMiao, Y., Hunter, A., & Georgilas, I. (2022). An Occupancy Mapping Method Based on K-Nearest Neighbours. Sensors, 22(1), 139. https://doi.org/10.3390/s22010139