🤖 AI Summary
This work addresses the challenge of high-speed quadrotor flight under limited onboard computational resources, where simultaneously satisfying actuator constraints and ensuring real-time safe obstacle avoidance remains difficult. The authors propose a nonlinear safety filter that incorporates full quadrotor dynamics, leveraging 3D Gaussian Splatting to construct a continuous geometric representation of the environment. For the first time, they introduce a high-relative-degree collision cone exponential control barrier function combined with a backup control barrier function, integrated with a forward-simulation-based backup strategy to guarantee feasibility of the input-constrained quadratic program (QP). This approach bridges the model-implementation gap between perception and control, enabling stable and real-time navigation in complex environments. Evaluated in both simulation and physical hardware, the method reduces trajectory jitter by 47% and achieves a 2.25× speedup compared to existing approaches.
📝 Abstract
Fast quadrotor flight requires safe obstacle avoidance under tight onboard compute limits. While 3D Gaussian Splatting (3DGS) provides a continuous, geometry-aware scene representation for perception-driven navigation, existing 3DGS safety filters use reduced-order models such as single- and double-integrators that ignore actuator limits and assume commanded accelerations are realized instantaneously. Building on an analytic collision cone barrier for 3DGS, we introduce a nonlinear, actuator-aware safety filter enforced through the full quadrotor dynamics. We derive a high-relative-degree collision cone exponential CBF and a backup CBF that preserves QP feasibility under input constraints using a forward-simulated backup policy. Compared with a state-of-the-art 3DGS safety filter, our approach reduces trajectory jerk by 47% and runs 2.25 times faster. We validate the method in simulation and on hardware for real-time navigation in cluttered, perception-derived environments.