Multi-Resolution Camera Clusters for High-Rate Depth Mapping
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Solution Overview
Problem
Autonomous vehicles face challenges in obtaining accurate and high-resolution depth data, especially in adverse weather conditions, due to limitations in existing LIDAR and RADAR technologies, which affect their ability to safely navigate and control environments.
Innovation Solution
A camera cluster system is employed, comprising multiple cameras with different resolutions and field of views, using parallax techniques to generate high-resolution depth maps that can replace some LIDARs, providing higher resolution and frame rates, and operating effectively in various weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR is used for distance measurements, then accurate depth data is obtained, but resolution is low and frame rate is low
Solution Approach 1:
The patent combines multiple cameras with different resolutions and fields of view into a camera cluster system. By merging the data from these multiple cameras, the system achieves high-resolution depth maps at high frame rates (up to 60 fps), resolving the contradiction between measurement precision and productivity that plagues traditional LIDAR systems.
Solution Approach 2:
The patent segments the imaging task across multiple cameras with different resolutions. High-resolution cameras capture detailed views of specific regions, while lower-resolution cameras provide broader coverage. This segmentation allows the system to achieve high overall resolution without requiring every camera to operate at maximum resolution, thereby maintaining high frame rates.
2Measurement precision
If LIDAR is used for long distance measurements, then accurate distance data is obtained, but cost is high and power consumption is high
Solution Approach 1:
The patent changes the operational parameters of the imaging system by using multiple cameras with different resolutions and fields of view. Lower-resolution cameras consume less power while still providing useful depth information through computational methods. This parameter change allows the system to maintain measurement precision across long distances without the high power consumption associated with traditional LIDAR.
Solution Approach 2:
The patent replaces the active illumination and mechanical scanning system of LIDAR with a passive optical system using multiple cameras. This substitution eliminates the need for high-power laser sources and moving parts, significantly reducing power consumption while maintaining distance measurement accuracy through computational stereo vision and depth map generation.
3Reliability
If RADAR is used for all-weather operation, then reliability in adverse conditions is improved, but resolution is low
Solution Approach 1:
The patent merges data from multiple cameras with different fields of view and resolutions to create comprehensive depth maps. By combining the strengths of various camera types (including those sensitive to different wavelengths), the system achieves high resolution while maintaining reliability in adverse weather conditions, overcoming the resolution limitation of RADAR.
4Measurement precision
If multiple high-resolution cameras are used to increase depth map resolution, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by using high-resolution cameras only in regions where detailed depth information is most critical, while using lower-resolution cameras for broader coverage areas. This selective allocation of resolution resources reduces overall device complexity while maintaining high measurement precision where it matters most.
Solution Approach 2:
The patent segments the scene into multiple regions of interest, each captured by cameras optimized for that specific region. This segmentation allows the system to achieve high overall resolution without requiring a single complex ultra-high-resolution camera, thereby reducing device complexity while maintaining measurement precision.
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
The camera cluster system significantly enhances the accuracy and reliability of depth data generation, enabling safer navigation and control of autonomous vehicles by providing high-definition videos with depth values at up to 60 fps and scalable images with 8 to 16-bit depth range, while reducing hardware costs and power consumption compared to traditional LIDAR and RADAR systems.
Implementation Method 1
A low-resolution depth map for each scene may be generated for each scene using the captured 2D images of the set of multi-resolution cameras using relatively small depths. A high-resolution depth map for each scene for a wide depth range may be generated for each scene by iteratively refining the low-resolution depth map for each scene.
Data Source
AI summary
In one example, a method may include capturing two-dimensional (2D) images of scenes using a set of multi-resolution cameras disposed on at least one side of an autonomous vehicle. Further, a low-resolution depth map with relatively small depths may be generated for each scene using the captured 2D images. Furthermore, a high-resolution depth map may be generated for each scene for a wide depth range by iteratively refining the low-resolution depth map for each scene. Also, a 3D video may be generated based on the high-resolution depth maps and the captured 2D images of the central camera. Further, a distance, a velocity, and/or an acceleration of one or more objects relative to the autonomous vehicle is computed by analyzing one or more frames of the 3D video. Then, the autonomous vehicle may be controlled based on the computed distance, velocity, and/or acceleration of the one or more objects.