Multi-Base Stereo Camera Depth Estimation
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Solution Overview
Problem
Conventional stereo camera systems have limited scan zones and poor depth accuracy at both close and far distances, making them unsuitable for long-range depth estimation applications, such as robotics handling and manipulation in facilities like warehouses.
Innovation Solution
A multi-base imaging system with at least three imaging sensors, where the second baseline is larger than the first, allowing for depth estimation using different sensor combinations for close and far distances, and stitching algorithms to generate accurate point cloud data and depth information across a larger field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional stereo camera systems are used, then device complexity is low, but scan zone coverage and depth accuracy are limited
Solution Approach 1:
The system divides the scene into multiple depth ranges (close range and far range) and uses different sensor pairs for each range. The first sensor pair handles close-range depth estimation while the second sensor pair handles far-range depth estimation, allowing each pair to be optimized for its specific range rather than requiring a single complex system to handle all ranges equally well
Solution Approach 2:
The patent transitions from a conventional two-sensor stereo system to a multi-base system with at least three sensors arranged in multiple baselines. This adds a dimensional aspect to depth estimation by utilizing multiple baseline lengths (first baseline and second baseline), enabling the system to estimate depth across a much broader range by switching between different baseline configurations
2Measurement precision
If conventional stereo camera systems are used, then manufacturing cost is low, but depth accuracy at close and far distances deteriorates
Solution Approach 1:
The system dynamically selects which sensor pair to use based on the depth range of the target object. For close-range objects, the first sensor pair is activated; for far-range objects, the second sensor pair is activated. This dynamic adaptation allows the system to maintain high depth accuracy across varying distances without requiring all sensors to operate at maximum complexity simultaneously
Solution Approach 2:
Each sensor pair is optimized for specific local conditions - the first sensor pair is optimized for close-range depth estimation while the second sensor pair is optimized for far-range depth estimation. This local optimization ensures that each subsystem operates at peak performance for its designated range, improving overall measurement precision without requiring uniform complexity across the entire system
3Reliability
If multi-base imaging system with multiple sensors is used, then scan zone and depth accuracy are improved, but device complexity increases
Solution Approach 1:
The system segments the depth estimation task into multiple specialized subsystems (first sensor pair for close range, second sensor pair for far range). This segmentation improves reliability by ensuring that each subsystem is optimized for its specific operational range, while the modular structure manages complexity through clear division of labor rather than requiring a monolithic complex system
Solution Approach 2:
The multi-base imaging system achieves multi-functionality by using the same physical platform to perform both close-range and far-range depth estimation through different sensor pairs. This universal approach improves reliability across different operational scenarios while managing complexity through a unified system architecture that handles multiple functions
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 multi-base imaging system achieves a significantly larger scan zone with improved depth accuracy compared to conventional stereo cameras, enabling effective long-range depth estimation in robotics applications.
Implementation Method 1
obtain a first image of a scene from a first imaging sensor, a second image of the scene from a second imaging sensor, and a third image of the scene from a third imaging sensor
Data Source
AI summary
Systems, techniques, and devices for performing long range depth estimation are described. Multiple images of a scene are captured via at least three imaging sensors of a camera device. A first imaging sensor is separated from a second imaging sensor by a first baseline, and a third imaging sensor is separated from the first imaging sensor by a second baseline larger than the first baseline. A first point cloud is generated based on a first image and a second image captured by the first imaging sensor and the second imaging sensor, respectively. A second point cloud is generated based on the first image and a third image captured by the first imaging sensor and the third imaging sensor, respectively. Depth information of the scene is generated based on the first point cloud and the second point cloud.