Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments
Abstract
:1. Introduction
1.1. State of the Art
- inertial sensors and a digital map of the mine [54];
- PF-based fusion of localization data from inertial sensors, gyroscopes, speed sensors and ultrasonic sensors [55];
- a neural network that takes a video stream as input [58];
- a combination of low-cost sensors, namely a UWB sensor, a beacon, an IMU and a magnetic field sensor, using Wi-Fi signals [59]; or
- IMU data, LiDAR data and color camera images [60].
1.2. Novelty
- a novel localization algorithm in which a 3D triangular mesh map is used as the reference for localization,
- robot pose correction calculations for each LiDAR measurement in the triangular mesh,
- serial and parallel-serial implementations of the algorithm, and
- an evaluation of the proposed algorithm on data obtained from cave and mine gallery environments.
2. Material and Methods
2.1. Localization
- prediction of the robot’s position and orientation based on inertial navigation sensors and prior knowledge concerning localization and
- updating of the robot’s position relative to the closest triangle in the map to the LiDAR scan point.
Map Search
- find the corresponding cell on the 2D surface,
- find the eight neighboring cells,
- find all mesh vertices within a radius R of point P,
- find the corresponding vertices of the 3D triangular mesh, and
- for each point within radius R, take all triangles that have point P as a vertex and choose the closest among them.
Algorithm 1: triangleSearch method. |
|
2.2. Ekf Procedure
3. Experimental
- the first section introduces the serial algorithm, where our initial solution to the problem is discussed;
- the second section introduces the parallel algorithm, where our method inspired by [69] is discussed; and
- the third section introduces the parallel-serial algorithm, where our innovative approach for satisfying given time constraints is detailed.
3.1. Serial Algorithm
- prediction;
- map search;
- calculation of the innovation, derivative, and Kalman gain; and
- updating.
Algorithm 2: Serial Kalman method. |
|
3.2. Parallel Algorithm
- prediction,
- map search, and
- the calculation of the innovation and derivative
Algorithm 3: Parallel algorithm. |
|
3.3. Parallel-Serial Algorithm
Algorithm 4: Parallel-serial Kalman method. |
|
4. Calculation
4.1. Hardware
- an NVIDIA Jetson TX2 and
- an NVIDIA Jetson Xavier AGX.
4.2. Testing Environments
5. Results
- scanner angular speed: 600 rpm,
- scanner sampling frequency: 300,000 points per second,
- max. scan size per single registration: 10,000 points,
- linear resolution of registration: 1 mm,
- heading angular resolution of registration: 0.01 deg,
- innovation permissible value in EKF linear part: 0.25 m.
6. Discussion
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Dataset | Point Cloud Size | Triangle Mesh Size | GMM Size | Total Distance Travelled |
---|---|---|---|---|
Mine gallery | 360,150 kpoints 4121.5 MB | 997,884 triangles 500,466 points 10.3 MB | 500,466 cells 3.8 MB | 121.916 m |
Cave | 144,060 kpoints 1648.6 MB | 1,727,729 triangles 865,824 points 19.2 MB | 325,254 cells 2.5 MB | 45.294 m |
Dataset | Mine Gallery | Cave | ||||||
---|---|---|---|---|---|---|---|---|
Method | P-S | P | GMM | PSD | P-S | P | GMM | PSD |
RMSE [cm] | 1.07 | 2.71 | 3.06 | 3.94 | 2.96 | 4.99 | 4.32 | 6.32 |
Max. dev. [cm] | 3.89 | 4.37 | 8.09 | 9.75 | 5.94 | 12.29 | 7.84 | 12.66 |
Machine | Parallel-Serial EKF (GPU) | Serial EKF (CPU) | GMM (CPU) |
---|---|---|---|
Jetson TX2 | 250,000 points/s | 80,000 points/s | 6000 points/s |
Jetson Xavier | 450,000 points/s | 80,000 points/s | 6000 points/s |
No. Measurements Points/s· 103 | 300 | 150 | 75 | 37.5 | 18.75 | 9.375 |
---|---|---|---|---|---|---|
Max. dev. [cm] | 5.94 | 5.85 | 6.92 | 7.81 | 9.08 | 79.83 |
Avg error [cm] | 2.75 | 2.76 | 2.77 | 2.83 | 3.66 | 13.75 |
RMSE [cm] | 2.96 | 2.97 | 3.04 | 3.14 | 4.17 | 22.31 |
Machine | GPU Efficiency | CPU Efficiency |
---|---|---|
Jetson TX2 | 16.7 points/mJ | 5.3 points/mJ |
Jetson Xavier | 15.4 points/mJ | 4.1 points/mJ |
PC | 11.3 points/mJ | 3.4 points/mJ |
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Niedzwiedzki, J.; Niewola, A.; Lipinski, P.; Swaczyna, P.; Bobinski, A.; Poryzala, P.; Podsedkowski, L. Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments. Sensors 2020, 20, 7123. https://doi.org/10.3390/s20247123
Niedzwiedzki J, Niewola A, Lipinski P, Swaczyna P, Bobinski A, Poryzala P, Podsedkowski L. Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments. Sensors. 2020; 20(24):7123. https://doi.org/10.3390/s20247123
Chicago/Turabian StyleNiedzwiedzki, Jakub, Adam Niewola, Piotr Lipinski, Piotr Swaczyna, Aleksander Bobinski, Pawel Poryzala, and Leszek Podsedkowski. 2020. "Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments" Sensors 20, no. 24: 7123. https://doi.org/10.3390/s20247123
APA StyleNiedzwiedzki, J., Niewola, A., Lipinski, P., Swaczyna, P., Bobinski, A., Poryzala, P., & Podsedkowski, L. (2020). Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments. Sensors, 20(24), 7123. https://doi.org/10.3390/s20247123