Hydromap creates detailed water elevation maps for inland waterway navigation

HydroMap: Probabilistic Water Surface Elevation Mapping for Semantic Scene Representation in Inland Waterways

Robotics

Summary

Self-driving boats need to understand not just their surroundings but also the surface of the water to navigate safely. However, traditional laser-based mapping often misses data on the water surface. The authors present HydroMap, a new method that uses stereo cameras to measure water surface height and combine it with maps of nearby structures. This approach builds a detailed, always-updated map of both water and surroundings, helping boats navigate more reliably in canals and rivers.

What this means in practice

Authors

Zhongbi Luo, Yunjia Wang, Herman Bruyninckx, Peter Slaets

Abstract

Autonomous surface vehicles operating in inland waterways require a persistent representation of both surrounding structures and the water surface. LiDAR-based simultaneous localization and mapping often produces sparse or missing water returns, leaving this operational surface absent from the reconstructed scene. We propose HydroMap, an odometry-decoupled framework that reconstructs water surface elevation from stereo observations and integrates it with the structural map. Per-frame water points form joint cell observations with propagated stereo and pose uncertainty, and successive observations are fused into a persistent probabilistic elevation map. Semantic map conversion then combines the elevation map with structural geometry in a unified 2.5D representation of water, boundaries, structures, and overhead regions. On the Pohang Canal and Leuven Vaart datasets, the elevation RMSE remains below 5 cm relative to LiDAR references expressed in the same map frame. The elevation and semantic maps are published at 2 Hz and 1 Hz, respectively. HydroMap thereby complements LiDAR maps with a persistent representation of the water surface for downstream navigation in inland waterways.