On fusing active and passive acoustic sensing for simultaneous localization and mapping

TL;DR

This paper investigates the benefits of combining active and passive acoustic sensing for simultaneous localization and mapping (SLAM), demonstrating that fusion can improve environmental perception especially with limited active sensing angles.

Contribution

It introduces models for fused active-passive acoustic sensing systems and evaluates their effectiveness in SLAM using EKF and FastSLAM 2.0 methods.

Findings

Fused sensing performs as well or better than active sensing alone.

Fused sensing perceives more environment when active sensing is limited.

Results align with previous studies on sensing accuracy.

Abstract

Studies on the social behaviors of bats show that they have the ability to eavesdrop on the signals emitted by conspecifics in their vicinity. They can fuse this ``passive" data with actively collected data from their own signals to get more information about their environment, allowing them to fly and hunt more efficiently and to avoid or cause jamming when competing for prey. Acoustic sensors are capable of similar feats but are generally used in only an active or passive capacity at one time. Is there a benefit to using both active and passive sensing simultaneously in the same array? In this work we define a family of models for active, passive, and fused sensing systems to measure range and bearing data from an environment defined by point-based landmarks. These measurements are used to solve the problem of simultaneous localization and mapping (SLAM) with extended Kalman filter (EKF) and FastSLAM 2.0 approaches. Our results show agreement with previous findings. Specifically, when active sensing is limited to a narrow angular range, fused sensing can perform just as accurately if not better, while also allowing the sensor to perceive more of the surrounding environment.