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Sensor Localization by Few Distance Measurements via the Intersection of Implicit Manifolds
Wednesday, January 4th, 4:10pm Tel Aviv time (3:10pm CET, 9:10am NY time)
Michael Bilevich, Tel Aviv University
Abstract:
Robot localization is the task of determining a robot’s position and orientation (pose) inside an environment using data collected from sensors. As such, localization is a key ingredient in robot navigation and other tasks, which has received a lot of attention over the years.
In this talk we will present a general approach for determining the unknown (or uncertain) pose of a sensor mounted on a robot in a known environment, using only a few distance measurements (between 2 to 6 typically), which is advantageous, among others, in sensor cost, and storage and information-communication resources.
We will discuss the underlying geometry of the problem at hand, and how it can be utilized to derive a simple-to-implement and easy-to-parallelize algorithm for numerical estimation of the sensor pose using a few distance measurements.
We will demonstrate the real-time effectiveness of our method even at high accuracy on a variety of scenarios and different allowable intermediate motions between measurements. We will also present experiments with a physical robot.
This is a joint work with Steven M. LaValle and Dan Halperin.
To appear in ICRA 2023.
