  We revisit the Safety Velocity Cones (SVCs) obstacle avoidance approach for
real-time autonomous navigation in an unknown $n$-dimensional environment. We
propose a locally Lipschitz continuous implementation of the SVC controller
using the distance-to-the-obstacle function and its gradient. We then show that
the proposed implementation guarantees safe navigation in generic environments
and almost globally asymptotic stability (AGAS) of the desired destination when
the workspace contains strongly convex obstacles. The proposed computationally
efficient control algorithm can be implemented onboard vehicles equipped with
limited range sensors (e.g., LiDAR, depth camera), allowing the controller to
be locally evaluated without requiring prior knowledge of the environment.
