Publication: Efficient obstacle detection and approach control for Micro air Vehicles using optical flow and texture cues.
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Date
2026-09
Authors
Shi, Xu
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Abstract
Micro air vehicles (MAVs) operating in confined and GPS-denied environments require lightweight visual methods for short-range approach control and obstacle triggered stopping. Optical-flow divergence provides a compact cue for regulating approach motion, but it remains primarily a motion-related signal and does not explicitly determine whether an observed appearance change corresponds to an obstacle. This limitation motivates the integration of an additional appearance-based cue. This thesis proposes a monocular onboard framework that combines constant divergence optical-flow control with texton-based visual appearance analysis. The optical-flow module regulates the MAV forward approach using sparse motion cues, while the texton-based module identifies sustained appearance deviations through a compact texture dictionary, chi-square histogram comparison, and temporal confirmation criterion. Both modules operate in parallel on a monocular smart camera at a resolution of 640 × 480 pixels and an onboard processing rate of approximately 10–15 Hz, without dense environmental reconstruction, deep-learning inference, or external image processing. The framework was evaluated in six indoor flight trials using divergence setpoints of
−0.1, −0.2, and −0.3 rad/s. With the obstacle positioned approximately 3.2 m from the forward-flight starting point, the MAV stopped after travelling approximately 2.4–3.0 m, leaving a remaining separation of approximately 0.2–0.8 m. The recorded divergence responses showed approximate approach regulation under the imposed forward-velocity constraint, while the texton-based module generated obstacle-triggered stopping events when the appearance-deviation criterion persisted for the required duration. Spatial likelihood maps generated from representative static images further provided qualitative visualizations of local obstacle-related texture responses. Overall, the results provide preliminary evidence that sparse optical-flow divergence and compact statistical appearance cues can be integrated onboard for short-range MAV approach regulation and obstacle-triggered stopping under the tested indoor, low-speed conditions. The findings demonstrate the feasibility of the proposed integration on a resource-constrained platform, but do not establish general detection reliability or
robustness across broader obstacle types, environments, lighting conditions, or flight speeds.