Shapes from Echoes: Uniqueness from Point-to-Plane Distance Matrices

Abstract

We study the problem of localizing a configuration of points and planes from the collection of point-to-plane distances. This problem models simultaneous localization and mapping from acoustic echoes as well as the notable "structure from sound" approach to microphone localization with unknown sources. In our earlier work we proposed computational methods for localization from point-to-plane distances and noted that such localization suffers from various ambiguities beyond the usual rigid body motions; in this paper we provide a complete characterization of uniqueness. We enumerate equivalence classes of configurations which lead to the same distance measurements as a function of the number of planes and points, and algebraically characterize the related transformations in both 2D and 3D. Here we only discuss uniqueness; computational tools and heuristics for practical localization from point-to-plane distances using sound will be addressed in a companion paper.

arXiv comment13 pages, 13 figures

References in corpus (6)

• Euclidean Distance Matrices: Essential Theory, Algorithms and Applications
• Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
• A Belief Propagation Algorithm for Multipath-Based SLAM
• Two Dimensional Array Imaging with Beam Steered Data
• Look, no Beacons! Optimal All-in-One EchoSLAM
• Omnidirectional Bats, Point-to-Plane Distances, and the Price of Uniqueness