Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles
Abhishek Goudar, Angela P. Schoellig
AI summary
Problem
Centralized control scales poorly and external localization systems are often unavailable, while existing decentralized methods typically require strict coordination, high-rate data exchange, or ignore sensor uncertainty.
Approach
The authors model cooperative localization and distance-based formation control as joint inference problems on factor graphs, solved asynchronously onboard each MAV using block coordinate descent and continuous-time Gaussian process motion priors.
Key results
- Decentralized block coordinate descent for MAP inference using range, visual, and inertial sensors
- Probabilistic formation control formulation that incorporates state estimation uncertainty
- Decimeter-level positioning and formation accuracy in real-world trials
- Successful onboard joint localization and navigation on resource-constrained MAVs in diverse environments
Why it matters
Enables robust, infrastructure-free multi-robot coordination for critical applications like search and rescue in GPS-denied or communication-restricted environments.
Abstract
Controlling a team of robots in a coordinated manner is challenging because centralized approaches (where all computation is performed on a central machine) scale poorly, and globally referenced external localization systems may not always be available. In this work, we consider the problem of range- aided decentralized localization and formation control. In such a setting, each robot estimates its relative pose by combining data only from onboard odometry sensors and distance measurements to other robots in the team. Additionally, each robot calculates the control inputs necessary to collaboratively navigate an en- vironment to accomplish a specific task, for example, moving in a desired formation while monitoring an area. We present a block coordinate descent approach to localization that does not require strict coordination between the robots. We present a novel formulation for formation control as inference on factor graphs that takes into account the state estimation uncertainty and can be solved efficiently. Our approach to range-aided localization and formation-based navigation is completely decentralized, does not require specialized trajectories to maintain formation, and achieves decimeter-level positioning and formation control ac- curacy. We demonstrate our approach through multiple real experiments involving formation flights in diverse indoor and outdoor environments.