Image-LiDAR Depth Completion for Dense Vehicle Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting objects and determining trajectories due to limited range and low density of sensor data, leading to reduced accuracy and precision in object information.
Innovation Solution
The implementation of a depth completion algorithm that projects lidar depth data into image data using a least squares optimization method, allowing for the determination of dense depth data and improved object detection, segmentation, and classification, while conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sparse sensor data is used to reduce computational load, then processing speed improves, but measurement precision and object detection accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary computational process (depth completion algorithm) that bridges sparse LiDAR depth data and dense image data. This intermediary step generates virtual depth values for pixels without direct LiDAR measurements by leveraging spatial correlations and intensity information, thereby achieving dense depth representation without requiring dense physical sensors
Solution Approach 2:
The patent creates virtual copies of depth information by propagating measured depth values from LiDAR points to corresponding image pixels through projection and interpolation. This copying process generates synthetic depth data for regions not directly observed by LiDAR, effectively multiplying the information content from the original sparse measurements
2Measurement precision
If dense sensor data is collected to improve object detection accuracy, then measurement precision improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by performing depth completion only for regions where LiDAR data is sparse rather than processing all pixels uniformly. The algorithm selectively interpolates depth values based on local geometric constraints and intensity gradients, applying computational effort proportionally to the information gap rather than uniformly across the entire image
Solution Approach 2:
The patent segments the image processing task into regions with direct LiDAR measurements and regions requiring interpolation. By dividing the computational domain based on data availability and processing each segment with appropriate algorithms (direct mapping vs. iterative optimization), the system reduces overall computational complexity while maintaining accuracy in critical regions
3Measurement precision
If LiDAR sensor density is increased to improve depth data coverage, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent makes the existing LiDAR sensor perform multiple functions by combining its depth measurements with intensity information from the same sensor and correlating both with image data. This multi-functional approach extracts maximum information from a single LiDAR sensor, replacing the need for additional dedicated depth sensors
Solution Approach 2:
The patent merges data from multiple sources (LiDAR depth values, LiDAR intensity values, and image pixel intensities) into a unified depth completion framework. By combining these complementary data streams and processing them jointly through optimization algorithms, the system achieves dense depth coverage using a single LiDAR sensor rather than requiring multiple sensors
Data Source
AI summary
Techniques for utilizing a depth completion algorithm to determine dense depth data are discussed are discussed herein. Two-dimensional image data representing an environment can be captured or otherwise received. Depth data representing the environment can be captured or otherwise received. The depth data can be projected into the image data and processed using the depth completion algorithm. The depth completion algorithm can be utilized to determine the dense depth values based on intensity values of pixels, and other depth values. A vehicle can be controlled based on the determined depth values.