Quadrotor navigation improves with adaptive gnss trust in cities
Belief-Adaptive Online Autonomy for Quadrotor UAV Navigation under GNSS Degradation in Urban Environments
Robotics
Summary
GNSS signals that help drones know their position can get messy in cities because of tall buildings causing errors and delays. The authors created a way for drones to automatically adjust how much they rely on these signals by tracking trust in real time. This system helps drones fly more smoothly and accurately even when GPS signals are unreliable, without needing prior knowledge or extra training. It fits into existing drone control systems to make flying in urban areas safer and more reliable.
What this means in practice
- •For drone operators: Enhance quadrotor flight stability and accuracy in urban airspaces by adapting GPS trust dynamically without prior environmental data.
- •For autonomous vehicle engineers: Integrate belief-adaptive online autonomy to improve sensor fusion reliability under degraded GNSS conditions in urban settings.
Authors
Deepak Kumar Panda, Weisi Guo
Abstract
Reliable online autonomy is critical for quadrotor operation in urban airspaces, where global navigation satellite systems (GNSS) measurements suffer from multipath, blockage, and latency issues, introducing non-stationary, temporally correlated errors that degrade conventional GNSS-IMU fusion. This paper presents a belief-adaptive online autonomy framework that augments an extended Kalman filter (EKF) with explicit GNSS trust modelling, second-order online belief adaptation, and latency-aware out-of-sequence measurement handling. GNSS trust is represented as a latent belief state that modulates measurement weighting and multipath bias uncertainty, and is updated online using EKF consistency signals. Unlike reactive covariance tuning, the proposed approach enables proactive and stable sensor trust adaptation without prior environmental knowledge or offline training. Evaluation in simulated urban air mobility scenarios with correlated multipath, stochastic latency, and obstacle constraints demonstrates improved belief convergence, smoother trajectories, and reduced estimation and tracking errors compared to naive, adaptive, and first-order baselines. The framework preserves classical GNSS-IMU fusion structure and can be integrated directly into existing flight control pipelines, supporting robust online autonomy in GNSS degraded environments.