Removable LiDAR Pod Self-Calibration Across Vehicle Types
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Solution Overview
Problem
Existing autonomous vehicles face challenges in collecting and interpreting environmental data efficiently, particularly when using removable sensors that require precise alignment and positioning on various vehicle types, which can lead to data collection inefficiencies and increased costs.
Innovation Solution
A removable automotive lidar data collection pod system that includes sensors like LIDAR, cameras, and IMU, mounted using adjustable mounts, collects data and determines its own position using 3D positioning sensors, allowing diverse data collection without vehicle customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If removable sensors are mounted on various vehicle types, then data collection versatility is improved, but sensor alignment precision deteriorates
Solution Approach 1:
The system performs self-calibration by autonomously determining its own position on the vehicle using 3D positioning sensors and comparing observed features with stored map data, eliminating the need for manual alignment procedures
Solution Approach 2:
The patent replaces mechanical alignment systems with computational methods, using computer vision and GPS/3D positioning to achieve precise sensor positioning without physical adjustment mechanisms
2Manufacturing precision
If vehicle customization is required for sensor mounting, then sensor alignment precision is improved, but system complexity and costs increase
Solution Approach 1:
The system is designed to be universally compatible with multiple vehicle types without customization, using software-based calibration that adapts to different mounting configurations rather than requiring hardware modifications
Solution Approach 2:
The system creates a digital model of the vehicle environment through map data and uses this copy to calculate and correct sensor positioning, eliminating the need for physical customization
3Measurement precision
If manual alignment procedures are used, then sensor positioning accuracy is improved, but data collection time increases
Solution Approach 1:
The system performs calibration automatically during the data collection process itself, using pre-stored map data to guide the self-alignment procedure rather than requiring separate manual alignment steps before data collection
Solution Approach 2:
The system autonomously determines its own position and orientation on the vehicle through self-calibration algorithms that process 3D positioning data and compare it with stored environmental maps
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
Enhances data collection diversity, reduces overfitting in machine learning models, and lowers costs by enabling data collection on any vehicle type, while ensuring accurate sensor alignment and expanded field of view.
Implementation Method 1
external removable pod hardware 102 can include a variety of sensors to collect environmental data. In a variety of implementations, a pod sensor suite can include LIDAR unit 104
Implementation Method 2
In a variety of implementations, one or more sensors within the removable pod can determine various measurements including: removable pod location measurements 504
Data Source
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AI summary
A vehicle agnostic removable pod can be mounted on a vehicle using one or more legs of a pod mount. The removable pod can collect and time stamp a variety of environmental data as well as vehicle data. For example, environmental data can be collected using a sensor suite which can include an IMU, 3D positioning sensor, one or more cameras, and/or a LIDAR unit. As another example, vehicle data can be collected via a CAN bus attached to the vehicle. Environmental data and/or vehicle data can be time stamped and transmitted to a remote server for further processing by a computing device.