Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones
This enables autonomous navigation for low-cost toy drones in indoor environments without external aids, though it is incremental as it combines existing requirements into a single solver.
The paper tackles the Perspective-n-Point problem for real-time visual navigation on weak IoT devices, achieving provable theoretical guarantees for both running time and correctness, with implementation on a DJI Tello Drone using a Raspberry PI Zero for autonomous indoor navigation.
The Perspective-n-Point problem aims to estimate the relative pose between a calibrated monocular camera and a known 3D model, by aligning pairs of 2D captured image points to their corresponding 3D points in the model. We suggest an algorithm that runs on weak IoT devices in real-time but still provides provable theoretical guarantees for both running time and correctness. Existing solvers provide only one of these requirements. Our main motivation was to turn the popular DJI's Tello Drone (<90gr, <\$100) into an autonomous drone that navigates in an indoor environment with no external human/laptop/sensor, by simply attaching a Raspberry PI Zero (<9gr, <\$25) to it. This tiny micro-processor takes as input a real-time video from a tiny RGB camera, and runs our PnP solver on-board. Extensive experimental results, open source code, and a demonstration video are included.