PROJECT · AUTONOMOUS SYSTEMS

Chronos.

An autonomous reconnaissance drone that surveys an area on its own, detects people and vehicles, places each of them on a map, and reports what it found, with all perception running onboard.

Why

Small drones are now everywhere, but most still need a pilot watching a video feed. Chronos focuses on the layer that removes that bottleneck: onboard perception and autonomy. The drone turns raw camera images into a clean, de-duplicated map of what is on the ground, with an honest estimate of how certain it is, so that an operator gets information instead of footage.

Applications include search and rescue, site security, infrastructure inspection and situational awareness.

What already works

The full perception pipeline runs today in simulation (PX4 autopilot and Gazebo), on a quadcopter flying autonomously.

~20 ms real-time object detection per frame on GPU
0.14 m median error when placing a detected object on the ground
7 / 7 objects found during an autonomous area survey (4 people, 3 vehicles)
Top-down map of the simulated area: the drone's lawnmower flight path, and every detected person and vehicle placed next to its true position.
Autonomous area survey, in simulation. The drone flies a lawnmower pattern at 5 m (red line) and builds this map live. Orange dots are people, blue dots are vehicles, crosses are the true positions; circles show the remaining position uncertainty. Result: 4 / 4 people and 3 / 3 vehicles found, median position error 0.14 m.
Top-down map of a survey flown at 12 m with a gimbal camera: four people and two of three vehicles placed next to their true positions; one vehicle, c2, is missing.
Same survey at 12 m, with a stabilised gimbal camera. People are now seen from almost straight above. Result: 4 / 4 people, 2 / 3 vehicles, median position error 0.17 m, one duplicate (vehicle c3 counted twice). The missing vehicle, c2, is a car model the detector never saw during training: it was found and confirmed early in the flight, then dropped when the detector stopped seeing it for several seconds while it was in plain view. The map prefers forgetting an object to inventing one; keeping every confirmed object would have recovered c2 but left ten false alarms on this flight. The detector used here was fine-tuned on simulated images to show the full pipeline; real flights need a detector trained on real aerial footage (VisDrone, then images from our own drone).
  • Flight
    Autonomous mission

    Take-off, stable hover, scan and landing, fully scripted.

  • Perception
    Detection and geolocation

    Every detection is projected from the camera image to a position on the ground, for any position and attitude of the drone.

  • Mapping
    Object registry

    Repeated sightings of the same object are fused into a single entry with an uncertainty estimate; false alarms fade out when the drone looks again and sees nothing.

Roadmap

  • Next
    Following a moving target

    Tracking a moving person or vehicle in the map (position and velocity) and steering the drone and gimbal to keep it in view.

  • Then
    Detector for real footage

    The simulation detector only proves the pipeline. For real flights: a detector trained on real aerial datasets, then refined on images from our own drone.

  • Then
    Physical prototype

    Quadcopter with an onboard computer and a stabilised gimbal camera, running the same software as in simulation.

  • Then
    Field tests

    Real flights on an open test site, measuring detection and positioning accuracy outdoors.

  • Later
    Learned navigation

    Flying without GPS and through obstacles, with navigation policies trained in simulation.

Support the project

Chronos is moving from simulation to a real prototype. If you can help with equipment, a test site, advice or an introduction, see what the project needs.

Needs & partnerships → Get in touch ↗