Dense Depth Estimation Using Machine Learning and LIDAR
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Solution Overview
Problem
Existing camera systems, such as stereoscopic and time-of-flight cameras, face challenges in accurately determining depth information due to alignment issues, limited range, and inaccuracies, which hinder effective depth estimation in various environments.
Innovation Solution
A method and system that utilize a machine learning algorithm trained with both image data and LIDAR data to generate dense depth estimates and confidence values for each pixel, enabling accurate depth perception even in environments where LIDAR data is sparse or absent, by integrating RGB data with LIDAR indicators and depth measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereoscopic cameras or time-of-flight cameras are used to capture depth information, then depth data can be obtained, but alignment and calibration become difficult and accuracy is reduced
Solution Approach 1:
The patent replaces complex mechanical alignment and calibration systems with a machine learning-based depth estimation system. Instead of relying on precise physical alignment of multiple cameras or time-of-flight sensors, the system uses a neural network trained on image data to automatically estimate depth, eliminating the need for manual calibration and reducing hardware complexity while maintaining or improving accuracy.
2Measurement precision
If specialized depth cameras are used, then depth information can be captured, but the range is limited and accuracy suffers
Solution Approach 1:
The patent creates a universal depth estimation system that can handle various environments and ranges using a single machine learning model. The neural network is trained on diverse image data to generalize across different scenarios, making the system adaptable to various depths and conditions without requiring specialized hardware configurations or multiple specialized cameras for different ranges.
3Measurement precision
If LIDAR data is used for depth estimation, then accurate depth measurements can be obtained, but processing power and memory requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for depth estimation from the input data, rather than processing complete high-resolution LIDAR point clouds. The machine learning model processes extracted image features and LIDAR indicators to generate depth estimates, significantly reducing computational load and memory requirements while maintaining accuracy by focusing on the most relevant information.
4Loss of information
If dense depth estimates are generated for each pixel, then comprehensive depth information is provided, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the depth estimation process into efficient computational stages using a machine learning architecture that processes image data through multiple layers to generate dense per-pixel depth estimates. The system divides the computational task into manageable operations including feature extraction, indicator generation, and depth calculation, enabling comprehensive depth coverage while controlling complexity through optimized neural network operations.
Data Source
AI summary
Systems, devices, and methods are described for generating dense depth estimates, and confidence values associated with such depth estimates, from image data. A machine learning algorithm can be trained using image data and associated depth values captured by one or more LIDAR sensors providing a ground truth. When the algorithm is deployed in a machine vision system, image data and/or depth data can be used to determine dense depth estimates for all pixels of the image data, as well as confidence values for each depth estimate. Such confidence values may be indicative of how confident the machine learned algorithm is of the associated depth estimate.