Navigation System Using Camera-LIDAR Fusion for GPS-Denied Positioning
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Solution Overview
Problem
Existing navigational systems, such as GPS, are limited in accuracy and availability in contested environments or urban areas with obstructions, making it difficult to determine location and orientation, especially for autonomous vehicles and dismounted soldiers, due to bulkiness, cost, and computational intensity of current solutions.
Innovation Solution
A low SWAP-C (Size, Weight, and Power - Cost) apparatus and method using an imaging device and a range-measuring device, like a laser range finder or LIDAR, to determine a user's location and orientation by generating a 3D frustum based on a geo-located model, comparing synthetic and actual images, and refining estimates with a range sensor, allowing for accurate navigation and targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two spaced-apart cameras are used for stereo optical triangulation to improve downrange accuracy, then measurement precision improves, but device complexity and size increase
Solution Approach 1:
The patent transitions from 2D image plane analysis to 3D spatial reasoning by incorporating depth information from a LIDAR sensor. This allows the system to use a single camera while achieving accurate downrange positioning through volumetric matching of the camera's field of view frustum with LIDAR point cloud data, eliminating the need for bulky stereo camera setups.
Solution Approach 2:
The patent combines data from multiple sensing modalities (electromagnetic radiation from camera and light time-of-flight from LIDAR) into a unified navigation solution. By merging these different types of spatial data and processing them together through 3D frustum modeling and point cloud matching, the system achieves accurate positioning without requiring multiple cameras spaced apart.
2Measurement precision
If LIDAR sensors with direct 3D-to-3D registration are used to improve accuracy, then measurement precision improves, but use of energy and computational resources increase
Solution Approach 1:
The patent extracts and utilizes only the essential depth information from LIDAR measurements that is directly relevant to position estimation. Rather than performing exhaustive 3D-to-3D registration of entire point clouds, the system focuses on matching the camera's field of view frustum with relevant portions of the LIDAR point cloud, significantly reducing computational requirements while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary generation of the camera field of view frustum model using known camera parameters and initial position estimates before matching with LIDAR data. This pre-processing step organizes the spatial relationships in advance, enabling more efficient and less computationally intensive matching operations when comparing with LIDAR point cloud measurements.
3Measurement precision
If depth-based cameras are used to obtain range information, then measurement precision improves, but adaptability to different lighting conditions deteriorates
Solution Approach 1:
The patent introduces a LIDAR sensor as an intermediary device that actively measures depth through time-of-flight of laser pulses. This intermediary sensing modality is independent of ambient illumination conditions, providing reliable range information that complements the passive electromagnetic radiation-based camera, which is sensitive to lighting conditions.
Solution Approach 2:
The patent changes the physical parameter used for depth measurement from passive light intensity detection (camera) to active light time-of-flight measurement (LIDAR). This parameter change enables accurate range measurement that is invariant to ambient illumination conditions, as the LIDAR system uses its own active light source and measures the time for light to travel to and from targets.
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 provides accurate location and orientation determination with minimal additional hardware, leveraging existing systems for navigation in GPS-challenged regions, enhancing navigation and targeting capabilities while maintaining portability and efficiency.
Implementation Method 1
a range-measuring device, like a laser range finder or LIDAR
Data Source
AI summary
A low SWAP-C apparatus and method enable determining precise location and orientation in a GPS-denied environment. A camera image of a scene is registered to a synthetic image predicted according to an initial estimate of location and orientation and a 3D model of the environment to obtain an accurate cross-plane location estimate perpendicular to the camera pointing direction, and an approximate downrange location in the pointing direction. A range sensor is then used to correct and refine the downrange estimate. The steps can be iterated until a required accuracy is attained. The camera can be an electro-optical or infrared imaging system. The range sensor can be a laser range finder or a LIDAR. The initial location estimate can be based on inertial measurements and/or earlier GPS readings. The registration can include applying a photogrammetric bundle-adjustment process. The disclosure is applicable to navigation, weapons pointing, and situational awareness.