Camera-Based Vehicle Localization with Crowdsourced Pavement Mapping
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Solution Overview
Problem
Traditional maps for paved surfaces, such as parking lots, are often costly and infrequently updated, and there is a lack of affordable and precise maps available for vehicles, as they typically require time-consuming sensor-based land surveys and infrequent updates from mapping entities.
Innovation Solution
A system using imaging sensors, vehicle odometry sensors, and a mapping engine to generate maps and localize vehicles on paved surfaces, incorporating crowdsourcing to update maps through vehicle-generated data, allowing vehicles to create and share maps with each other, thereby reducing logistical costs and enhancing map accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based land surveys are used to create maps, then map accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system enables vehicles to autonomously capture images and generate their own map data using onboard imaging sensors and processing units, eliminating the need for external surveying teams and equipment while maintaining map accuracy through self-collected visual data
Solution Approach 2:
Traditional mechanical surveying equipment is replaced with electronic imaging sensors and computer vision algorithms that process visual data to create and update maps, significantly reducing time consumption while maintaining or improving accuracy through digital image processing
2Reliability
If traditional mapping entities update maps, then map reliability is improved, but update frequency decreases and cost increases
Solution Approach 1:
Vehicles equipped with the mapping system continuously capture and process images to automatically update map data in real-time, eliminating dependence on periodic updates from external mapping entities while maintaining reliability through distributed validation across multiple vehicles
Solution Approach 2:
The system enables continuous map updates as vehicles traverse the environment, transforming discrete periodic updates into a continuous process where map data is constantly refined and improved through ongoing data collection from multiple sources
3Productivity
If crowdsourced map generation is implemented, then cost is reduced and update frequency increases, but system complexity increases
Solution Approach 1:
The imaging sensor system serves multiple functions including primary navigation, map generation, and environmental monitoring, allowing a single hardware platform to support crowdsourced mapping without requiring dedicated specialized equipment for each function
Solution Approach 2:
A centralized server acts as an intermediary that receives images from multiple vehicles, performs coordinated processing and validation, and distributes updated maps back to vehicles, managing the complexity of crowdsourced data integration centrally while keeping individual vehicle systems relatively simple
Data Source
AI summary
A system for generating a map of a paved surface for a vehicle and localizing the vehicle on the map of the paved surface includes an imaging sensor, a vehicle odometry sensor, a memory, a processor, and a transceiver. The imaging sensor captures a series of image frames. The vehicle odometry sensor measures an orientation, a velocity, and an acceleration of the vehicle. The memory stores a mapping engine as computer readable code. The processor executes the mapping engine to generate a map. The transceiver uploads the map to a server such that the map is accessed by a second vehicle that uses the map to traverse the external environment.