Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

Area of Science:

• Robotics
• Sensor Networks
• Simultaneous Localization and Mapping (SLAM)

Background:

• Existing Range-Only SLAM methods often treat sensor nodes (beacons) as passive, neglecting their capabilities.
• Simultaneous Localization and Mapping is computationally intensive and faces resource limitations in many applications.
• Resource constraints in robots and sensor nodes necessitate efficient SLAM algorithms.

Purpose of the Study:

• To develop a scalable, distributed RO-SLAM scheme for resource-constrained environments.
• To enhance SLAM accuracy by enabling robot-beacon cooperation.
• To manage resource consumption by setting a bound on integrated measurements per iteration.

Main Methods:

• A Sparse Extended Information Filter (SEIF) SLAM approach is employed.
• Beacons actively gather and integrate robot-beacon and inter-beacon measurements.
• A distributed, information-driven measurement allocation tool dynamically selects measurements to balance accuracy and resource use.

Main Results:

• The proposed scheme achieves significant estimation accuracies.
• Demonstrates high resource-consumption efficiency and scalability.
• Experimental validation on an octocopter Unmanned Aerial System (UAS) in 3D outdoor environments showed robustness and advantages over existing methods.

Conclusions:

• The cooperative, distributed RO-SLAM scheme effectively addresses resource constraints.
• Beacon participation in measurement selection, gathering, and integration improves SLAM performance.
• The system offers a robust and scalable solution for real-world applications, particularly on Unmanned Aerial Systems.