Monocular Depth Estimation with Sparse Range Sensor Uncertainty
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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 depth estimation method using a depth model that incorporates sparse range sensor data, particularly from radar sensors, along with uncertainty derived from noise and sparsity, to improve depth estimation accuracy in monocular images.
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 introduces an intermediary processing pipeline that combines monocular image data with depth model predictions and range sensor uncertainty estimates. The system uses a depth estimation network that takes monocular images and incorporates uncertainty information from range sensors to produce improved depth maps, effectively mediating between the low cost of monocular cameras and the need for accurate depth information.
Solution Approach 2:
The patent changes the parameters fed into the depth estimation network by incorporating uncertainty estimates as additional input channels. Instead of using only monocular images, the system combines image data with uncertainty parameter maps derived from range sensor data, allowing the network to weigh different regions appropriately based on their uncertainty levels.
2Measurement precision
If range sensor data is used for depth estimation, then depth information is obtained, but sparsity and noise are introduced
Solution Approach 1:
The patent converts the harmful sparsity and noise in range sensor data into beneficial uncertainty estimates. By calculating uncertainty maps from the range sensor data and feeding these into the depth estimation network, the system transforms the problematic sparsity and noise into useful information that guides the network in weighting and fusing depth predictions, effectively turning the harm into a benefit.
Solution Approach 2:
The system implements feedback by using uncertainty estimates derived from range sensor data to adjust the depth estimation process. The uncertainty maps provide feedback information that allows the depth estimation network to dynamically adjust its predictions, weighting reliable regions higher and uncertain regions lower, creating a self-correcting system.
3Measurement precision
If LiDAR sensors are used for depth perception, then depth accuracy is improved, but cost and weather sensitivity increase
Solution Approach 1:
The patent employs a more economical approach by using monocular cameras combined with computational depth estimation rather than expensive LiDAR sensors. The system achieves acceptable depth accuracy through intelligent image processing and uncertainty-based fusion, effectively replacing expensive hardware with software-based solutions that are more weather-resistant and cost-effective.
Data Source
AI summary
Systems and methods are provided for depth estimation from monocular images using a depth model with sparse range sensor data and uncertainty in the range sensor as inputs thereto. According to some embodiments, the methods and systems comprise receiving an image captured by an image sensor, where the image represents a scene of an environment. The method and systems also comprise deriving a point cloud representative of the scene of the environment from range sensor data, and deriving range sensor uncertainty from the range sensor data. Then a depth map can be derived for the image based on the point cloud and the range sensor uncertainty as one or more inputs into a depth model.