Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation
Abstract
:1. Introduction
- development of a distributed robot-beacon tool that selects the most informative measurements that are integrated in SLAM fulfilling the resource consumption bound;
- extension to 3D SLAM, integration and experimentation of the scheme with an octorotor UAS;
- new experimental performance evaluation and comparison with existing methods;
- new subsection with experimental robustness evaluation;
- extension and more detailed related work. Furthermore, the paper has been restructured and all sections have been completed and rewritten for clarity.
2. Related Work
2.1. Range Only SEIF SLAM in a Nutshell
2.2. Integration of Range Measurements
3. Problem Formulation
4. Operation of the Robot
Algorithm 1: Summary of the operation of the robot. |
Require:
|
5. Operation of Beacons
Algorithm 2: Summary of the operation of beacon |
|
5.1. Measurement Allocation
5.2. Integration of Measurements
6. Experiments
6.1. Validation
6.2. Performance Comparison
6.3. Discussion
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Nomenclature
Covariance matrix of the SLAM global state at time t | |
Mean of the SLAM global state at time t | |
Updated information matrix of the SLAM global state at time t | |
Updated Information vector of the SLAM global state at time t | |
Predicted information matrix and predicted information vector of the SLAM global state for time t | |
Update contribution of beacon to | |
Measurement gathered by the robot to beacon . Measurement gathered by beacon to | |
Observation models for robot-beacon and inter-beacon measurements | |
Jacobians of the observation models for robot-beacon and inter-beacon measurements | |
Sets of the beacons that are currently within the sensing region of the robot and beacon , respectively | |
List with the number of measurements assigned to each beacon in in measurement distribution | |
Set of measurements gathered by beacon | |
Maximum number of measurements that can be gathered and integrated per SLAM iteration | |
Utility function for measurement | |
Reward and cost for measurement | |
Weighting factor between reward and cost |
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M1 | M2 | M3 | Proposed | |
---|---|---|---|---|
Map RMS error (m) | 0.49 | 0.33 | 0.34 | 0.34 |
Robot RMS error (m) | 0.59 | 0.49 | 0.50 | 0.51 |
PF convergence times (s) | 25.2 | 5.4 | 5.6 | 5.7 |
# of measurements/iteration | 33.2 | 206.9 | 80 | 61.7 |
Beacon energy consumption (J) | 43.7 | 272.2 | 105.2 | 81.1 |
Robot CPU time (% of M1) | 100 | 65.6 | 265.5 | 58.6 |
Map RMS error (m) | 0.35 | 0.346 | 0.34 |
Robot RMS error (m) | 0.52 | 0.51 | 0.51 |
PF convergence times (s) | 15.8 | 9.5 | 5.7 |
# of measurements/iteration | 40 | 56.5 | 61.7 |
Map RMS error (m) | 0.34 | 0.34 | 0.37 |
Robot RMS error (m) | 0.51 | 0.51 | 0.52 |
PF convergence times (s) | 5.7 | 5.7 | 5.9 |
# of measurements/iteration | 78.9 | 61.7 | 49.3 |
PRR = 40 | PRR = 60 | PRR = 80 | PRR = 100 | |
---|---|---|---|---|
Map RMS error (m) | 0.4 | 0.37 | 0.35 | 0.35 |
Robot RMS error (m) | 0.57 | 0.53 | 0.52 | 0.51 |
PF convergence times (s) | 9.6 | 7.1 | 6.4 | 5.7 |
# of measurements/iteration | 44.8 | 51.4 | 57.1 | 61.7 |
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Torres-González, A.; Martínez-de Dios, J.R.; Ollero, A. Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation. Sensors 2017, 17, 903. https://doi.org/10.3390/s17040903
Torres-González A, Martínez-de Dios JR, Ollero A. Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation. Sensors. 2017; 17(4):903. https://doi.org/10.3390/s17040903
Chicago/Turabian StyleTorres-González, Arturo, Jose Ramiro Martínez-de Dios, and Anibal Ollero. 2017. "Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation" Sensors 17, no. 4: 903. https://doi.org/10.3390/s17040903