LIDAR Point-Cloud Cache Retrieval for Triggered Event Analysis
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Solution Overview
Problem
LIDAR systems generate large amounts of data rapidly, leading to the overwrite of previously generated point clouds and object identification information, making it difficult to access historically acquired data for recreating environmental and system conditions, especially after triggering events such as system malfunctions or accidents.
Innovation Solution
A LIDAR system with a cache memory that stores point-cloud representations and navigational information, allowing for the retrieval of archived data within a predetermined period relative to triggering events, such as system malfunctions or accidents, to assist in diagnosing faults and reconstructing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LIDAR systems continuously generate and store point cloud data, then data availability for analysis is improved, but storage requirements and system complexity increase
Solution Approach 1:
The patent extracts only the essential navigation parameters (position, orientation, velocity) from the complete point cloud data and stores these extracted features in a database. This allows retrieval of historical navigation information without storing the entire raw point cloud datasets, thereby reducing storage requirements while maintaining the ability to analyze historical conditions.
Solution Approach 2:
The system pre-processes and stores key navigation parameters in advance during normal operation. When a triggering event occurs (such as an accident or system malfunction), the pre-stored navigation information is immediately available for analysis, eliminating the need to process and store vast amounts of raw point cloud data retrospectively.
2Loss of information
If all generated point cloud data is retained in memory, then access to historical data is improved, but memory usage and computational overhead increase
Solution Approach 1:
The system extracts and stores only the essential navigation parameters (position, orientation, velocity) from the complete point cloud data in a database, rather than storing all raw point cloud data. This dramatically reduces the volume of stored data while preserving the critical information needed for reconstructing historical conditions.
Solution Approach 2:
The patent implements a selective storage approach where only certain key parameters are stored in the database, while complete point cloud data is retained only for recent periods or under specific conditions. This partial storage strategy reduces overall data volume while maintaining sufficient information for diagnostic purposes.
3Productivity
If point cloud data is overwritten to make space for new data, then storage efficiency is improved, but ability to retrieve historical data deteriorates
Solution Approach 1:
The system extracts and stores essential navigation parameters (position, orientation, velocity) in a database alongside the point cloud data. This extracted information persists even when raw point cloud data is overwritten, ensuring historical navigation context is preserved for analyzing past system conditions and events.
Solution Approach 2:
The patent creates a simplified copy of the essential navigation information from the point cloud data and stores this copy in a database. This copy serves as a persistent record that survives the overwriting of original point cloud data, enabling historical analysis without requiring the original large-volume data to be retained.
4Loss of information
If extensive storage capacity is provided for historical data, then data retrieval capability is improved, but system cost and complexity increase
Solution Approach 1:
The system extracts and stores only the essential navigation parameters (position, orientation, velocity) in a database, rather than storing complete point cloud data for extended periods. This extracted information provides sufficient detail for analyzing historical system conditions and events while requiring minimal storage infrastructure.
Solution Approach 2:
The system pre-stores key navigation parameters in a database during normal operation. When historical analysis is needed following a triggering event, this pre-stored information is immediately available, eliminating the need for extensive storage capacity or complex data retrieval systems.
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 efficient storage and quick access to historical LIDAR data for analyzing system conditions before, during, and after triggering events, reducing the need for extensive storage and improving diagnostic capabilities.
Implementation Method 1
at least one sensor configured to detect laser light reflections from objects in the field of view of the LIDAR system
Implementation Method 2
A light detection and ranging system, (LIDAR a/k/a LADAR) is an example of technology that can work well in differing conditions, by measuring distances to objects by illuminating objects with light and measuring the reflected pulses with a sensor
Data Source
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AI summary
A LIDAR system is disclosed. The LIDAR system may include at least one light source configured to project laser light toward a field of view of the LIDAR system, at least one sensor configured to detect laser light reflections from objects in the field of view of the LIDAR system, and at least one processor configured to perform operations. The processor may be configured to use the laser light reflections to generate point-cloud representations of an environment of the LIDAR system within the field of view of the LIDAR system, output navigational information based on the generated point-cloud representations to one or more processors associated with a vehicle on which the LIDAR system is mounted, and store at least some of the generated point-cloud representations in a cache memory to provide a point-cloud archive. The processor may further be configured to detect occurrence of a point-cloud archive output triggering event, in response to detection of the point-cloud archive output triggering event, collect from the cache memory two or more point clouds from the point-cloud archive that were generated within a predetermined period of time relative to the detected point-cloud archive output triggering event, and output the two or more point clouds collected from the cache memory.