river field robotics

autonomous navigation in dynamic aquatic environments

Field robotics brings the challenges of the real world directly to our algorithms. River environments present unique difficulties: strong currents, variable water conditions, limited visibility, and the unpredictability of natural settings. Our work focuses on enabling robust autonomous navigation and monitoring in these dynamic freshwater ecosystems.

On-site during river experiments, preparing autonomous systems for deployment in challenging freshwater environments.

The challenge

Rivers are deceptively complex environments for autonomous robots:

  • Dynamic flow fields: Currents vary with depth, location, and time
  • Limited localization: GPS signals can be unreliable near vegetation and water
  • Sensor challenges: Water clarity, reflections, and foam affect vision systems
  • Safety constraints: Operating near infrastructure, wildlife, and changing water levels
  • Environmental monitoring: Collecting meaningful scientific data while navigating
Deploying and testing navigation algorithms in real river conditions - where theory meets practice.

Our approach

Our research combines robust SLAM algorithms with adaptive control to handle the uncertainties of river navigation:

  1. Multi-sensor fusion: Integrating IMU, GPS, visual odometry, and flow sensors
  2. Robust state estimation: Using our CORA backend to handle noisy measurements
  3. Current compensation: Adapting control strategies to flow conditions
  4. Environmental mapping: Building maps for both navigation and scientific purposes

From theory to practice

Field deployments are where we validate our algorithms against reality. The gap between simulation and real-world performance drives us to develop more robust outlier rejection methods, better uncertainty quantification, adaptive algorithms that learn from the environment, and fail-safe behaviors for unexpected situations.

Every field test provides invaluable insights that improve our algorithms for both freshwater and marine applications.

Broader impact

This work contributes to freshwater ecosystem monitoring, climate science through understanding river dynamics, infrastructure inspection, and search and rescue operations.

This research bridges the gap between cutting-edge SLAM algorithms and real-world environmental monitoring applications.