UAV Visual Beacon Alignment for Coordinate System Synchronization
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Solution Overview
Problem
Existing visual navigation systems for unmanned aerial vehicles (UAVs) face challenges in accurately aligning coordinate systems, especially in GPS-denied environments or when operating at low altitudes, due to large viewpoint differences and varying visual appearances of objects, which complicates the tracking and combination of location data from multiple UAVs.
Innovation Solution
The system configures UAVs to identify and process other UAVs as visual references, using triangulation and visual beacons to determine relative positions, and employs a chain of transformations between devices to align coordinate systems, even when they are not in direct visual line of sight, utilizing cameras' directional sensitivity to enhance accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual features are tracked from multiple UAVs, then positioning information can be obtained, but accurate alignment of coordinate systems becomes difficult due to viewpoint differences and varying visual appearances
Solution Approach 1:
The patent introduces visual beacons as intermediary objects that are deliberately placed in the environment to serve as common reference points for multiple UAVs. These beacons provide stable, recognizable visual features that can be detected by multiple UAVs from different viewpoints, enabling accurate coordinate system alignment without requiring complex processing of natural environment features.
Solution Approach 2:
The patent utilizes visual beacons with distinct visual characteristics (colors, patterns, or lighting properties) that make them easily distinguishable from the natural environment. These visual markers provide high-contrast, recognizable features that can be reliably detected and tracked by multiple UAVs, solving the problem of varying visual appearances of natural features.
2Ease of operation
If multiple UAVs operate independently with local coordinate systems, then navigation autonomy is maintained, but combining location data from multiple UAVs becomes inaccurate
Solution Approach 1:
Visual beacons serve as intermediary reference objects that enable independent UAVs to determine their relative positions and orientations. Each UAV can independently detect the same beacons and use them to compute transformation parameters between coordinate systems, maintaining autonomy while achieving accurate data fusion.
Solution Approach 2:
The patent transforms the coordinate alignment problem from a 2D image space problem to a 3D spatial problem by using visual beacons with known physical dimensions and spatial configurations. This allows UAVs to compute not only 2D position but also orientation and depth information, enabling accurate 3D coordinate system alignment.
3Measurement precision
If visual beacons are used to align coordinate systems, then alignment accuracy is improved, but additional visual markers must be deployed in the environment
Solution Approach 1:
The visual beacons serve multiple functions: they act as reference markers for coordinate alignment, provide positioning targets for UAV navigation, and can serve as communication signals between UAVs. This multi-functionality reduces the need for additional specialized markers, as the same visual beacons fulfill multiple roles in the system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate alignment of coordinate systems, allowing for reliable object detection and localization across multiple UAVs, improving spatial accuracy and enabling collaborative missions, even in challenging environments like indoors or at night.
Implementation Method 1
An object detection system receives sensor data including at least one image of a scene captured by an imaging sensor
Data Source
AI summary
Systems and methods include tracking a current location of an unmanned device on a local map, receiving image data from an imaging sensor associated with the unmanned device, detecting a first remote device and outputting associated first detected object information, determining a first location of the detected first remote device on the local map, receiving first location information associated with a first remote coordinate system of the first remote device, the first location information corresponding to a location of the first remote device when the image data was captured, and determining a transformation between the local map and the first remote coordinate system. The local map may be aligned to a plurality of remote coordinate systems through triangulation based at least in part on location detections of a plurality of corresponding remote devices. The remote devices may generate a beacon or light that is detected by the system.