Monocular Depth Estimation for UAV Perch Landing
No depth sensor — just math. Recovering 3D pose from a single camera using image moments and projective geometry.
Error @ 25 cm
Error @ 1 m
Focal Length
Tech Stack
The Problem
A drone landing on a perch has no GPS signal, no laser rangefinder, and no dedicated depth sensor — just a camera. It needs to figure out exactly how far away the landing target is and whether it's correctly aligned, using only what appears in a single video frame. Get the math wrong, and the drone crashes.
The Challenge
Autonomous UAV landing on a perch requires knowing the perch's 3D position and the camera's attitude relative to it — without a depth sensor, IMU, or GPS. The system had to recover all of this from a single video frame using only the geometric properties of the detected blob: its centroid, principal axes, and apparent size. The depth estimation formula Z = f·X/X₀ is simple in principle but sensitive to blob detection noise, so the pipeline needed to be robust to partial occlusion and varying lighting.
Architecture & System Design
Single camera estimates 3D target pose using computer vision: image processing extracts target features, geometric math recovers position and orientation from 2D image moments. Enables closed-loop visual control without depth sensor.
The pipeline runs in real time on each video frame: (1) Convert BGR → HSV and threshold for the target colour (yellow: H=20–30, S=50–255, V=100–255) to isolate the blob. (2) Compute zeroth, first, and second-order image moments (M00, M10, M01, M20, M02, M11) to recover centroid (cx, cy) and shape covariance. (3) Eigendecompose the 2×2 covariance matrix to get principal axes and orientation angle θ — this gives camera roll/pitch relative to the perch. (4) Depth: Z = f · X_real / X_pixels, where focal length f=3.22 mm was measured via calibration. Output: [cx, cy, θ, Z] written to a position matrix file and used as the visual error signal for the PID controller. A complementary OpenCV toolkit was developed alongside: Canny edge detection with interactive threshold sliders, HSV range calibration tool, and SURF feature matching.
Code Walkthrough
3-step walk-through of the production implementation — file paths and intent shown above each block.
Results
Depth estimation accuracy: 0.67 cm error at 25 cm, 5.7 cm at 1 m, and 25 cm at 2 m — performance degrades linearly with distance as expected from the triangulation model. Orientation estimation successfully recovered camera attitude (roll/pitch) relative to the perch surface at all tested distances. The full pipeline ran at real-time video rates in C++ on a laptop. Results were validated against a Vicon motion capture ground-truth system and documented in the final project report.
Gallery & Demos
Blob Detection Result
HSV segmentation isolating the yellow perch marker with centroid, principal axes, and orientation angle overlaid.
Depth Estimation Geometry
Diagram showing focal-length triangulation: how 2D blob size in pixels maps to 3D distance using calibrated focal length.
Accuracy Performance Plot
Depth estimation error (mm) vs. true distance, showing sub-1cm accuracy within working range.
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Interested in this work?
Full architecture walkthrough and code review available during interviews.