Single-Camera Distance Estimation Using LiDAR-Guided Depth Learning
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Solution Overview
Problem
Conventional deep learning approaches for object detection and distance estimation in unmanned vehicles require large training datasets and struggle with limited field of view, leading to inaccuracies in depth estimation, especially in corner cases where LiDAR sensors fail to provide sufficient data.
Innovation Solution
A method and apparatus using a single camera for object detection and distance estimation, employing a YOLOv5 model for object detection and a depth estimation model trained with iterative learning and LiDAR projection data, with a fusion unit to combine camera and LiDAR data, enabling depth estimation without relying solely on LiDAR data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (camera and LiDAR) are used for object detection and distance estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a camera to capture optical images and creates a digital representation of the visual scene, then processes this optical data through deep learning models to extract depth and distance information that would traditionally require separate LiDAR sensors. This copying approach allows the system to obtain depth information from optical data alone.
Solution Approach 2:
The patent replaces the mechanical LiDAR sensing system with a camera-based optical system combined with computational algorithms. Instead of using active light emission and time-of-flight measurement mechanisms, the system uses passive optical capture and deep learning-based depth estimation to achieve distance measurement.
2Measurement precision
If LiDAR sensors are used for depth estimation, then measurement precision is improved, but the field of view is limited and corner cases cannot be detected
Solution Approach 1:
The patent makes the camera system multi-functional by enabling it to perform both object detection and depth estimation tasks that would traditionally require separate specialized sensors. The single camera system handles multiple functions including object recognition, distance measurement, and spatial mapping through different processing pipelines.
Solution Approach 2:
The patent transforms two-dimensional image data from the camera into three-dimensional depth information through deep learning models. By learning the mapping from 2D optical images to 3D spatial relationships, the system overcomes the inherent dimensionality limitation of camera sensors and achieves accurate depth estimation across the entire field of view.
3Measurement precision
If conventional deep learning approaches are used for object detection and distance estimation, then detection accuracy is improved, but large training datasets and extensive training time are required
Solution Approach 1:
The patent pre-trains depth estimation models using synthetic data generated from object detection models and depth maps. This preliminary training with artificially generated data prepares the system for real-world deployment, reducing the need for extensive training with large datasets of annotated real images and accelerating the overall training process.
Solution Approach 2:
The system uses its own object detection outputs and generated depth information to create training data for its depth estimation model. By leveraging internally generated data rather than relying entirely on external annotated datasets, the system reduces dependency on large external training resources and can iteratively improve its own performance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate object detection and distance estimation with limited training data, improving performance in corner cases and reducing costs by eliminating the need for multiple sensors, while maintaining high detection accuracy.
Implementation Method 1
a LIDAR configured to generate a projection image by analyzing three-dimensional spatial positions
Implementation Method 2
a camera configured to capture an image
Data Source
AI summary
The present disclosure relates to an apparatus and method for estimating a distance to an object. More particularly, the apparatus includes a camera for capturing surrounding images, a LiDAR for generating a projection image by analyzing surrounding three-dimensional spatial positions, an object detection model for detecting objects in the images, a depth estimation model for estimating distances to the objects, a training unit for training the object detection model and the depth estimation model, and an estimation unit for detecting the objects in the images and estimating the distances to the objects using the object detection model and the depth estimation model. The apparatus and method may detect objects and estimate distances to the objects by using only a single camera image.