Adaptive Ladar Shot Energy Control Using Spatial Return Indexes
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Solution Overview
Problem
Ladar systems in autonomous vehicles face challenges due to artifacts and noise in return data, which can corrupt training and classification processes, particularly from non-uniform illumination and variable environmental conditions, requiring adaptive control of shot energy and other parameters with low latency.
Innovation Solution
A ladar system stores prior return data in a spatial index, such as a quad tree, to enable rapid retrieval and analysis for adaptive control of shot energy and other parameters, ensuring uniform illumination and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ladar systems illuminate the field of view with ladar pulses to detect obstacles, then object detection capability is improved, but non-uniform illumination causes artifacts and noise that corrupt training and classification processes
Solution Approach 1:
The ladar system dynamically adjusts shot energy on a shot-by-shot basis using adaptive control. The system modifies illumination parameters in real-time based on feedback from previously acquired return data, transitioning from static uniform illumination to dynamic adaptive illumination that responds to actual scene conditions, thereby reducing artifacts while maintaining detection accuracy
Solution Approach 2:
The system implements feedback control by analyzing previously acquired return data to determine appropriate shot energy levels for subsequent pulses. The control system uses this feedback information to adjust illumination parameters, creating a closed-loop system that continuously optimizes illumination uniformity and reduces artifacts in the returned data
2Reliability
If ladar systems acquire dense return data from all range points to ensure complete coverage, then detection completeness is improved, but computational complexity increases significantly
Solution Approach 1:
The system applies local quality by determining shot energy on a local basis for each range point rather than using a global uniform approach. The adaptive control system evaluates return data characteristics specific to each location and adjusts illumination parameters locally, maintaining detection completeness while reducing overall computational complexity through spatially-varying processing
Solution Approach 2:
The system performs preliminary action by acquiring and storing return data in an efficient spatial index structure before final classification. This pre-processing organization allows for faster subsequent retrieval and analysis, reducing computational complexity in the classification stage while maintaining complete detection coverage
Data Source
AI summary
Disclosed herein are examples of ladar systems and methods where data about a plurality of ladar returns from prior ladar pulse shots gets stored in a spatial index that associates ladar return data with corresponding locations in a coordinate space to which the ladar return data pertain. This spatial index can then be accessed by a processor to retrieve ladar return data for locations in the coordinate space that are near a range point to be targeted by the ladar system with a new ladar pulse shot. This nearby prior ladar return data can then be analyzed by the ladar system to help define a parameter value for use by the ladar system with respect to the new ladar pulse shot. Examples of such adaptively controlled parameter values can include shot energy, receiver parameters, shot selection, camera settings, and others.