Time-Dependent Adjustable Filter for LIDAR Distance Estimation
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Solution Overview
Problem
Current LIDAR systems face challenges in achieving high accuracy and range due to fixed filters that either prioritize resolution or range, failing to effectively estimate distances of both near and far objects with high precision.
Innovation Solution
The implementation of a time-dependent adjustable filter in the LIDAR system, which adjusts its cutoff frequencies based on elapsed time since signal transmission and expected signal spreading, allowing for adaptive filtering of return signals to enhance accuracy and sensitivity across varying distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed filter with high cutoff frequency is used, then resolution for near objects is improved, but range for far objects deteriorates
Solution Approach 1:
The filter cutoff frequency is made dynamic rather than fixed. The system automatically adjusts the filter cutoff frequency based on the detected time of flight or distance range, switching between high cutoff frequencies for near objects and low cutoff frequencies for far objects. This dynamic adaptation resolves the contradiction by allowing the filter to optimize for resolution when measuring near objects and for range when measuring far objects.
Solution Approach 2:
The filter parameter (cutoff frequency) is changed based on the measurement conditions. The system modifies the filter cutoff frequency parameter according to the detected distance or time of flight, selecting appropriate frequency values from a set of predefined frequencies. This parameter adaptation enables the system to achieve high measurement precision for both near and far objects by matching the filter characteristics to the specific measurement scenario.
2Adaptability or versatility
If a fixed filter with low cutoff frequency is used, then range for far objects is improved, but resolution for near objects deteriorates
Solution Approach 1:
The filter cutoff frequency is made dynamic rather than fixed. The system automatically adjusts the filter cutoff frequency based on the detected time of flight or distance range, switching between low cutoff frequencies for far objects and high cutoff frequencies for near objects. This dynamic adaptation resolves the contradiction by allowing the filter to optimize for range when measuring far objects and for resolution when measuring near objects.
Solution Approach 2:
The filter parameter (cutoff frequency) is changed based on the measurement conditions. The system modifies the filter cutoff frequency parameter according to the detected distance or time of flight, selecting appropriate frequency values from a set of predefined frequencies. This parameter adaptation enables the system to achieve high measurement precision for both near and far objects by matching the filter characteristics to the specific measurement scenario.
3Adaptability or versatility
If time-dependent adjustable filter is implemented, then adaptability across distances is improved, but device complexity increases
Solution Approach 1:
The system uses feedback from the time of flight measurement or distance detection to automatically adjust the filter cutoff frequency. The detected range information feeds back to the filter control mechanism, which then selects the appropriate cutoff frequency from a set of predefined values. This feedback-based automatic adjustment improves adaptability across varying distances while minimizing the increase in device complexity by using a straightforward control logic.
Solution Approach 2:
The distance range is segmented into multiple zones, with each zone associated with a specific filter cutoff frequency. The system divides the detection range into near, medium, and far zones, and assigns optimized filter frequencies to each zone. This segmentation approach simplifies the filter adjustment mechanism by using discrete frequency steps rather than continuous adjustment, reducing complexity while maintaining adaptability.
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
This approach enables the LIDAR system to accurately estimate distances of both near and far objects, balancing resolution and range by dynamically adjusting the filter to account for signal strength and noise, thereby improving overall performance.
Implementation Method 1
The reflected energy is captured by an optical receiving element and is converted from light energy to an electrical signal
Data Source
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Figure 2A~2B
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AI summary
Methods, computer-readable media, and apparatuses for estimating a distance of an object from a Light Detection and Ranging (LIDAR) system is disclosed. In one embodiment, the method includes transmitting a first light signal towards the object using a LIDAR system, and detecting a second light signal by a light sensor to generate a detected signal. The second light signal includes a reflection of the first light signal from the object. The method further includes generating a filtered signal by applying a time-dependent adjustable filter to the detected signal, and estimating the distance of the object from the LIDAR system based at least on the filtered signal.