VL53L5CX Outlier values and Fluctuations
Firstly, on behalf of our company, I would like to appreciate the great products that have been delivered by STM through the years, wish STM well and prosper.
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Recently we are working on using the VL53L5CX TOF for our ML project.
Settings in fw :
I2C speed : 1MHz
Resolution : 8x8
Ranging Frequency : 2Hz
Ranging Mode : Continuous
Target Order : Closest
Sharpener Percentage : 20%
MCU : ESP-32
Platform : Arduino IDE
With Cover Glass
Room environment, curtains are opened

Graphing software : Processing Grapher
Constraints :
- Sensor will be installed on ceiling, facing the ground, around a distance of 2.5m to 3m
- Must use all 8x8 SPAD sensors
- Sharpener value should be around 20%, since we are planning to use all the SPAD, as the sharpener increases, edge values readings become vague

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Case 1 : Facing the ceiling, steady

Observation : Takes some time for the outlier values to return to normal value

Case 2 : Waved my hand once above the TOF
Observation : Some outlier values appear (purple blue around 0mm) after my hand are no longer on the TOF

Case 3 : With my hands 20cm on top of the TOF, steady
Observation : Values are fluctuating

Case 4 : TOF facing the pillows, steady

Observation :

We have also tried to have the TOF installed on the ceiling and facing the chairs & table etc, but the output are fluctuating as well.
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For Case 1 & 2, why do the outlier values take a long time to return to the normal reading position?
-> How do we have those outlier value returned to the normal position quick?
Our doubt is that, with case 3 & 4, since everything in the scenario is steady (not moving).
->Why is the output of the values fluctuating?
Our understanding to the sensor is that, if the TOF is facing/ sensing an area with non-moving/steady objects, it should return stable values, and values should not be fluctuating. Is it related to the settings in the fw?
We have tried to calibrate the sensors with the proposed setting by STM, i.e :

Also tried with and without the cover glass, but the fluctuation persists.
Stable values would be really helpful for building the ML model when collecting the data.
We look forward to the discussion. Thank you.
Cheers
Will
