Time-of-Flight Histogram Detection of Dirt on Lidar Windows
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Solution Overview
Problem
Dirt on the window of a distance measuring apparatus, such as a lidar, affects the signal-to-noise ratio of echo pulses, making it difficult to accurately measure distances.
Innovation Solution
A distance measuring apparatus with a light emitter, receiver, calculator, and determiner that determines dirt on the window based on a dirt determination condition, including a specified light intensity level at a specified time of flight, using a histogram to identify dirt and trigger a cleanup unit to remove it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the distance measuring apparatus operates normally without dirt detection, then the device complexity is low, but the measurement precision deteriorates due to dirt on the window
Solution Approach 1:
The system performs preliminary dirt detection by analyzing the histogram of received light intensities before distance measurement. The determiner checks whether the light intensity at a specified time of flight exceeds a threshold, identifying dirt presence in advance. This preliminary action allows the system to maintain measurement precision by detecting dirt before it significantly degrades distance measurement accuracy.
Solution Approach 2:
The patent introduces a histogram analysis mechanism as an intermediary between the receiver and the distance measurement process. The calculator generates a histogram from received light intensities, and the determiner uses this histogram to detect dirt. This intermediary analysis layer enables dirt detection without requiring additional hardware, resolving the contradiction by adding computational complexity rather than physical complexity.
2Reliability
If the light intensity threshold is set low to detect dirt, then the reliability of dirt detection improves, but false detection increases due to noise
Solution Approach 1:
The system dynamically adjusts the threshold parameter based on histogram analysis. Instead of using a fixed threshold, the determiner compares the light intensity at the specified time of flight against a threshold derived from the histogram distribution. This parameter adaptation allows the system to maintain high detection reliability while accounting for varying noise conditions and ambient light levels.
Solution Approach 2:
The histogram analysis provides feedback about the distribution of received light intensities, which is used to set appropriate detection thresholds. The system continuously monitors the light intensity distribution and adjusts its dirt detection criteria based on this feedback, enabling it to distinguish between actual dirt reflections and noise or ambient light variations.
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 accurate determination and removal of dirt on the window, ensuring reliable distance measurements by maintaining the signal-to-noise ratio of echo pulses.
Implementation Method 1
emits a light pulse, receives an echo pulse resulting from reflection of the light pulse by a target object, and measures the distance of the target object from the distance measuring apparatus as a function of time of flight
Implementation Method 2
receives an echo pulse resulting from reflection of the light pulse by a target object
Data Source
AI summary
A distance measuring apparatus includes a light emitter, a receiver, a calculator, a case having a window, and a determiner for determining that dirt is adhered to the window in response to determination that a dirt determination condition is satisfied. The dirt determination condition includes a first condition that a specified light intensity level at a specified value of a time of flight for at least one pixel of a view region in a histogram is larger than or equal to at least one value of an intensity threshold calculated for the at least one pixel of the view region.