Self-Supervised Depth Estimation for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles and robotic systems face challenges in accurately perceiving their environment due to limitations in depth estimation from camera sensors, such as depth ambiguities, limited resolution, and image artifacts, which hinder navigation and situational awareness.
Innovation Solution
A self-supervised depth estimation method using image frames from cameras mounted on vehicles, involving the prediction of depth maps, warping, projection, and loss calculation to update depth values, allowing for accurate depth estimation without relying on calibrated camera models or expensive LiDAR systems, and enabling learning from various camera geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera models are used for depth estimation, then depth information can be extracted from images, but depth ambiguities and limited resolution occur
Solution Approach 1:
The patent implements a self-supervised learning framework where the network uses feedback from image warping and projection operations to iteratively improve depth estimates. The predicted depth map is used to warp the image and project it back, with the reprojection error serving as feedback to refine the depth estimation, thereby reducing depth ambiguities without requiring additional sensors or calibrated camera models.
Solution Approach 2:
The system performs self-supervised learning where the depth estimation network uses its own predictions to generate training data through image warping and projection. The network serves itself by using the predicted depth map to create synthetic ground truth labels through the warping and projection process, eliminating the need for external LiDAR data or manual annotations.
2Measurement precision
If calibrated camera models are used, then depth estimation can be performed, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive and complex calibrated camera models with a learnable neural network that can be trained from uncalibrated images. Instead of requiring precise camera calibration data, the system uses a disposable-like approach where the network parameters are updated through self-supervised learning on raw images, achieving accurate depth estimation without the complexity of calibration procedures.
Solution Approach 2:
The patent substitutes the mechanical/optical camera calibration process with a computational neural network approach. Instead of physically calibrating camera intrinsics and extrinsics through complex algorithms, the system uses a deep learning model that learns the camera model parameters implicitly from image data, replacing the traditional mechanical calibration system with a software-based learning system.
3Measurement precision
If LiDAR systems are used, then accurate depth information can be obtained, but cost and device complexity increase
Solution Approach 1:
The patent creates a computational copy of LiDAR functionality using a neural network that processes camera images. Instead of using physical LiDAR sensors to directly measure depth, the system copies the depth extraction function through a learnable model that processes 2D images and produces 3D depth maps, achieving similar accuracy with simpler, more cost-effective camera hardware.
Solution Approach 2:
The patent replaces the mechanical LiDAR sensing system with a computational vision system based on neural networks. Instead of using active optical sensors to emit and detect light for direct depth measurement, the system uses a learnable neural network to infer depth from passive camera images, substituting the physical sensing mechanism with a computational model that achieves comparable depth estimation accuracy.
4Loss of information
If images are captured from multiple camera locations, then comprehensive environmental awareness can be achieved, but data processing complexity increases
Solution Approach 1:
The patent implements a universal neural network model that can process images from multiple camera locations and mounting configurations. The network is trained to handle various camera geometries and perspectives, making it multi-functional across different deployment scenarios. This universality allows the system to achieve comprehensive environmental awareness from multiple camera positions without requiring separate processing pipelines for each camera configuration.
Data Source
AI summary
Systems and methods for self-supervised depth estimation using image frames captured from a vehicle-mounted camera, may include: receiving a first image captured by the camera while the camera is mounted at a first location on the vehicle, the source image comprising pixels representing a scene of the environment of the vehicle; receiving a reference image captured by the camera while the camera is mounted at a second location on the vehicle, the reference image comprising pixels representing a scene of the environment; predicting a depth map for the first image comprising predicted depth values for pixels of the first image; warping the first image to a perspective of the camera at the second location on the vehicle to arrive at a warped first image; projecting the warped first image onto the source image; determining a loss based on the projection; and updating predicted depth values for the first image.