Robust range estimation using acoustic and multimodal sensing

TLDR

Fine‑grained localization benefits robotics and sensor networks, but individual sensing modes can be blocked or confused, motivating multimodal approaches for robust range estimation. The study presents an acoustic ranging system robust to interference yet vulnerable to non‑line‑of‑sight errors, and proposes using orthogonal sensor evidence to detect and discard such erroneous measurements. The authors implement an acoustic ranging system that measures distance via sound propagation and combine it with orthogonal sensory data to identify and filter non‑line‑of‑sight outliers.

Abstract

Many applications of robotics and embedded sensor technology can benefit from fine-grained localization. Fine-grained localization can simplify multi-robot collaboration, enable energy efficient multi-hop routing for low-power radio networks, and enable automatic calibration of distributed sensing systems. We focus on range estimation, a critical prerequisite for fine-grained localization. While many mechanisms for range estimation exist, any individual mode of sensing can be blocked or confused by the environment. We present and analyze an acoustic ranging system that performs well in the presence of many types of interference, but can return incorrect measurements in non-line-of-sight conditions. We then suggest how evidence from an orthogonal sensory channel might be used to detect and eliminate these measurements. The work illustrates the more general research theme of combining multiple modalities to obtain robust results.