Monocular Depth Estimation With Geometric Priors for BEV Localization
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Solution Overview
Problem
Accurately determining the positions or locations of detected objects in a semantically segmented birds-eye-view (BEV) is challenging due to the lack of accurate depth information in camera images, which is typically addressed by employing multiple sensors like radar and LIDAR, increasing cost and complexity.
Innovation Solution
A geometrical distance aware machine learning model is used to estimate depth from monocular camera images, reducing the need for multiple sensors by employing geometrical priors to improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors like radar and LIDAR are employed to obtain depth information, then localization accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical depth sensing mechanisms (LIDAR, radar) with a computational approach using machine learning models that process monocular camera images to infer depth information through geometrical priors and neural network transformations
Solution Approach 2:
The patent creates a virtual representation of depth information by transforming 2D camera images into 3D BEV space using learned geometrical relationships, effectively copying depth data without requiring physical depth sensors
2Measurement precision
If multiple sensors like radar and LIDAR are employed to obtain depth information, then localization accuracy is improved, but cost increases
Solution Approach 1:
The patent replaces expensive depth sensors with a single monocular camera that can be easily deployed, using computational methods to generate depth information that would otherwise require costly hardware
Solution Approach 2:
The patent substitutes physical sensing hardware with a software-based machine learning system that processes standard camera images to extract depth information, significantly reducing hardware costs
3Device complexity
If monocular camera images are used without depth sensors, then device complexity is reduced, but depth information accuracy deteriorates
Solution Approach 1:
The patent introduces machine learning models and geometrical priors as intermediary computational layers that process monocular camera images to infer accurate depth information, bridging the gap between simple camera input and accurate 3D output
Solution Approach 2:
The patent transforms the representation of spatial information by changing parameters from raw pixel coordinates to depth-aware BEV space, enabling accurate depth measurement through learned geometrical relationships rather than direct sensor measurement
Data Source
AI summary
Techniques and systems are provided for generating depth information for an image. For instance, a process can include obtaining one or more images of an environment. The process can further include generating a set of features for the one or more images. The process can also include combining the set of features with one or more distance maps to generate combined feature distance information, wherein the one or more distance maps indicate distances based on relative height above a ground level. The process can further include generating depth information of the environment based on the combined feature distance information, and outputting the depth information of the environment.