LIDAR-Camera Calibration Using Object Constraints for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in navigation due to inaccurate and outdated sensor data, particularly from conventional maps and GPS systems, which lack precision and freshness, and struggle with detecting essential road features like lanes, signs, and emergency vehicles, leading to safety concerns.
Innovation Solution
The development of high-definition (HD) maps that use real-time data from autonomous vehicles' sensors to create accurate and up-to-date spatial geometric information, allowing for precise navigation and interaction between LIDAR and camera sensors through user-assisted calibration, reducing inconsistencies and improving sensor alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional maps are used for navigation, then the system is simple to implement, but the accuracy and freshness of spatial information deteriorates
Solution Approach 1:
The patent combines multiple data sources including LIDAR sensor data, camera images, GPS information, and map data into a unified HD map system. This integration allows the system to achieve high spatial accuracy (within 30 cm) by fusing real-time sensor measurements with pre-existing map information, resolving the contradiction between accuracy and complexity through systematic data integration.
Solution Approach 2:
The system implements continuous feedback loops where autonomous vehicles collect real-time sensor data, compare it against existing HD maps, and update the maps with newly acquired information. This feedback mechanism ensures map freshness and accuracy while distributing the computational complexity across multiple vehicles and time periods, rather than requiring all processing in a single system.
2Measurement precision
If survey teams create comprehensive maps, then map accuracy improves, but the time and cost to maintain updates increases
Solution Approach 1:
The system enables autonomous vehicles to self-update map information by collecting sensor data during normal operations and automatically contributing it to the HD map database. Each vehicle serves as both a consumer and producer of map data, eliminating the need for dedicated survey teams to perform routine updates while maintaining high accuracy through continuous real-world validation.
Solution Approach 2:
The system performs preliminary data collection and validation during regular vehicle operations before integrating updates into the main HD map. By pre-processing sensor data during normal driving and preparing updates in advance, the system can rapidly incorporate map changes without requiring dedicated survey expeditions, thus improving update speed while maintaining accuracy.
3Difficulty of detecting and measuring
If LIDAR and camera sensors are used together, then detection capability improves, but sensor calibration complexity increases
Solution Approach 1:
The patent introduces an intermediary calibration process that uses identified objects (such as road signs, lane markers, or specific geometric features) as reference points to establish the spatial relationship between LIDAR and camera sensors. These intermediary objects serve as common reference frames that allow the system to calibrate multiple sensor types without requiring direct complex inter-sensor alignment, thus improving detection capability while managing calibration complexity.
4Loss of information
If real-time sensor data is collected continuously, then information freshness improves, but data storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential and changed information from continuous sensor data streams rather than archiving all raw data. By identifying and extracting key features (such as map geometry changes, new obstacles, or modified road features) and storing only these extracted elements, the system maintains information freshness while significantly reducing storage requirements compared to keeping complete continuous data streams.
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
This solution enables autonomous vehicles to navigate safely with high accuracy and up-to-date information, reducing latency and storage requirements while ensuring precise location determination and timely updates, thereby enhancing safety and efficiency.
Implementation Method 1
a LIDAR sensor used to capture a set of LIDAR points
Implementation Method 2
a camera used to capture an image
Data Source
AI summary
A method includes obtaining first user input identifying at least one LIDAR point in a set of LIDAR points associated with an object in an image, and obtaining second user input identifying the object in the image. The method may also include generating a constraint on a relationship between a LIDAR sensor used to capture the set of LIDAR points and a camera used to capture the image. The method may additionally include reducing a cost associated with the LIDAR point being inconsistent with the object in the image subject to the constraint.