LIDAR Point-Cloud Cache Retrieval for Triggered Event History
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Solution Overview
Problem
Existing LIDAR systems face challenges in reliably accessing and utilizing historically acquired point cloud information, particularly during and after triggering events, leading to potential loss or overwrite of valuable environmental data.
Innovation Solution
A LIDAR system with a cache memory to store generated point-cloud representations, allowing retrieval of archived data in response to triggering events, utilizing a processor to detect such events and output relevant point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LIDAR systems continuously generate and store point cloud data, then data availability for historical analysis is improved, but memory resources are consumed and system complexity increases
Solution Approach 1:
The system pre-stores point cloud data in cache memory before triggering events occur. The cache memory is continuously updated with recent point cloud representations, so when a triggering event is detected, the required historical data is already available in the cache without needing to retrieve it from long-term storage or regenerate it.
Solution Approach 2:
The cache memory stores point cloud data with different retention characteristics based on recency. More recent point cloud data is retained in the cache while older data is allowed to be overwritten or deleted. This creates a quality gradient where data availability is highest for recent time periods and decreases for older periods, optimizing the balance between data availability and memory usage.
2Productivity
If LIDAR systems overwrite old point cloud data to make room for new data, then memory resources are optimized, but access to historical data during triggering events is lost
Solution Approach 1:
The system prepares the cache memory in advance by continuously storing point cloud data up to a certain retention period. When a triggering event is detected, the system checks whether data from the relevant time period exists in the cache. If the event occurred within the retention period, reliable data is available; if not, the system recognizes the data is unavailable rather than attempting to retrieve it from overwritten storage.
Solution Approach 2:
The system monitors cache memory usage and data retrieval patterns to dynamically adjust the cache retention period. When triggering events frequently occur within the current retention period, the system maintains or extends the retention period to ensure data availability. When memory pressure increases or events occur outside the retention period, the system may reduce the retention period to optimize memory resources.
3Duration of action of stationary object
If LIDAR systems extend the retention period of point cloud data in cache, then historical data availability is improved, but memory resource consumption increases
Solution Approach 1:
The system dynamically adjusts the cache retention period parameter based on operational conditions. The retention period can be extended when memory resources are abundant and historical data access is valuable, and reduced when memory resources are constrained or when triggering events rarely occur within extended time windows. This allows the system to optimize the balance between data retention duration and memory resource consumption.
Solution Approach 2:
The system stores point cloud data for a retention period that may be longer than the minimum necessary for most operations, preparing for potential triggering events that could occur at any time. However, the cache is designed to overwrite older data after the retention period expires, so the system only maintains the excessive retention temporarily until either a triggering event occurs or the retention period elapses.
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 reliable access and utilization of historical LIDAR data, enhancing the system's ability to recreate environmental conditions and assist navigation, even under varying conditions.
Implementation Method 1
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.
Implementation Method 2
at least one sensor configured to detect laser light reflections from objects in the field of view of the LIDAR system
Data Source
AI summary
A LIDAR system includes a light source projecting laser light toward a field of view, a sensor detecting reflections from objects, and a processor. The processor generates point-cloud representations of the environment, outputs navigational information to vehicle processors, and stores the point-cloud representations in a memory as a point-cloud archive. Upon detecting a point-cloud archive output triggering event, the processor collects two or more point-clouds from the archive that were generated within a predetermined period of time relative to the detected event, and outputs them from the memory.