Geolocation with Range: Robustness, Efficiency and Scalability
Numerous geolocation technologies, such as GPS, can pinpoint a person’s or object’s position on Earth under ideal conditions. However, autonomous navigation of mobile robots requires a precision localization system that can operate under a variety of environmental and resource constraints. Take for example an emergency response scenario where a hospital building is on fire. This is a time sensitive life or death scenario where it is critical for first responders to locate possible survivors in a smoke filled room. The robot’s sensors need to work past environmental occlusions such as excessive smoke, debris, etc to provide support to the first responders. The robot itself also needs to effectively and accurately navigate the room with minimal help from other agents that might be present in the building since it is unrealistic to deploy unlimited robots for this task. The available resources need to be effectively used to best aid the rescue crew and ensure the safety of the rescue workers.
Scenarios such as this present a crucial need for solutions that can work effectively in the presence of environmental constraints that can interfere with a sensor while giving equal weighting to resource constraints that impact the localization ability of a robot. This thesis presents one such experimentally proven solution that offers superior accuracy, robustness and scalability demonstrated via several realworld robot experiments and simulations.
The geolocation technique explored uses a recently discovered sensor technology called the ranging radios that are able to communicate and measure range in the absence of line-of-sight between radio nodes. This provides a straightforward approach to tackle unknown occlusions in the environment and enables the use of range to localize the agent in a variety of different situations.
One shortcoming of range-only data created by these ranging radios is that they generate a nonlinear and multi-modal measurement distribution that existing estimation techniques fail to accurately and efficiently model. To overcome this shortcoming, a novel and robust method for localization and SLAM (Simultaneous Localization and Mapping) given range-only data to stationary feature/nodes is developed and presented here.
In addition to this centralized filtering technique, two key extensions are investigated and experimentally proven in order to provide a comprehensive framework for geolocation with range. The first is a decentralized filtering technique that distributes computational needs across several agents. This technique is especially useful in real-world scenarios where leveraging a large number of agents in an environment is not unrealistic. The second is a novel cooperative localization strategy, based on first principles, that leverages the motion of mobile agents in the system to provide better accuracy in a featureless environment. This technique is useful in cases where a limited number of mobile agents need to coordinate with each other to mutually improve their estimates.
The developed techniques offer a unified global framework for geolocation with range that spans everything from static network localization to multi-robot cooperative localization with a level of accuracy and robustness which no other existing techniques can provide