Sparse Radar Depth Fusion for Monocular Depth Estimation
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Solution Overview
Problem
Existing autonomous or semi-autonomous systems face challenges in accurately estimating depth from monocular images due to limitations in sensor data, such as high costs, sparsity, noise, and limited field-of-view, leading to reduced situational awareness and navigation difficulties.
Innovation Solution
A method and system that utilize sparse depth data from radar sensors, incorporating uncertainty estimation as a covariance matrix, to improve depth estimation by projecting this data onto a 2D image plane and inputting it into a depth model alongside monocular images, thereby filtering out noise and sparsity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If monocular cameras are used for depth estimation, then cost is reduced, but depth information is not explicitly included and processing becomes more difficult
Solution Approach 1:
The patent combines monocular camera images with sparse range sensor depth data to create a hybrid depth estimation system. The range sensor provides explicit depth information while the camera provides visual context, merging the advantages of both sensor types to achieve accurate depth estimation without requiring expensive high-fidelity sensors alone.
Solution Approach 2:
The patent introduces an intermediary processing system that fuses monocular image data with sparse range sensor data. This intermediary layer processes both data types, handles the uncertainty in range sensor measurements, and produces refined depth estimates that overcome the limitations of using either sensor type alone.
2Measurement precision
If LiDAR sensors are used for depth perception, then depth perception accuracy is improved, but cost increases and performance degrades in certain weather conditions
Solution Approach 1:
The patent replaces expensive LiDAR sensors with cheaper radar sensors that provide sufficient depth information for the application. The system accepts the lower precision of radar sensors but compensates through data fusion with camera images and uncertainty modeling, achieving acceptable performance at reduced cost.
Solution Approach 2:
The patent changes the sensor modality from optical (LiDAR) to electromagnetic (radar) and adjusts the operating parameters accordingly. By modeling the uncertainty characteristics of radar measurements and combining them with visual data, the system adapts to the different parameter space of radar sensors to achieve effective depth estimation.
3Ease of manufacture
If radar sensors are used for depth data, then cost is reduced, but data sparsity and noise increase
Solution Approach 1:
The patent implements uncertainty modeling that provides feedback about the reliability of each depth measurement. By quantifying the uncertainty in radar measurements and using this information to weight and filter data points during fusion processing, the system compensates for sparsity and noise through intelligent data selection and confidence-based processing.
Solution Approach 2:
The patent merges radar depth data with camera image data to compensate for the sparsity and noise inherent in radar measurements. The camera provides continuous visual information that fills in gaps and contextualizes radar measurements, creating a more complete and reliable depth representation through combination rather than relying on radar alone.
Data Source
AI summary
Systems and methods for depth estimation are provided. According to some embodiments, a method may comprise: (1) generating, based on range sensor data, a representation of a scene of an environment; (2) calculating, from the range sensor data, range sensor uncertainty of one or more points of the representation; (3) generating depth data by projecting the representation onto a 2D image plane; (4) generating blurred depth data by projecting the range sensor uncertainty onto the depth data; and (5) deriving a depth map for an image of the scene based on the blurred depth data.