Active Mapping of Underwater Litter Using Camera-Sonar Fusion

2026-09-30

AI Summary

This study addresses the limitations of single-sensor perception and inefficient path planning in low-visibility underwater environments by proposing an active mapping framework that fuses camera and sonar data. The method constructs a shared Bayesian occupancy map integrated with range-dependent detection probability modeling, and designs a dual-term utility scoring mechanism based on voxel entropy to balance exploration and exploitation, thereby enabling dynamic real-time optimization of observation viewpoints. Simulation results demonstrate that this fusion strategy significantly outperforms unimodal sensing approaches and achieves more efficient target localization compared to traditional lawnmower paths. This work provides a novel paradigm for autonomous exploration in complex underwater environments.

Abstract

Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.

Problem

• Marine litter
• Underwater mapping
• Active perception
• Camera-sonar fusion
• Autonomous survey

Innovation

• Active Mapping
• Camera-Sonar Fusion
• Bayesian Occupancy Map
• Next-Best-View
• Underwater Litter Detection