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Port of ST VL53L3CX (2020) driver library to Raspberry Pi

This is a port of the support library to Raspberry Pi for the VL53L3CX Time-of-Flight ranging sensor with advanced multi-object detection. Thanks to the advanced API of the 2020 edition of ST's library, you can extract a great deal of subtle information from what the laser sensor can see within its ~25 degree <= 5000 mm (indoor) <= 1600 mm (outdoor) field of view.

ST supply the original copy of this library at https://www.st.com/en/imaging-and-photonics-solutions/vl53l3cx.html#tools-software where the STSW-IMG015 download is this support library for the STM32 Nucleo board. Also at the same link is the STSW-IMG021 download which is a Linux kernel driver formulation of the same code. This port takes the Nucleo library and repoints it at userspace I2C for Raspberry Pi, as a kernel driver is way overkill for this sensor.

I should emphasise that this is not a high quality port, I only did just enough porting to get the example to compile and link, and I didn't bother with rigourous error handling either. But if all you need is the same high level ranging API the example uses, it should be enough.

Related alternatives:

  • https://github.com/stm32duino/VL53L3CX is for STM32 and Arduino, however the VL53L3CX library they used as their base is the 2018 edition, which is a completely different codebase and much less sophisticated. It exposes considerably less detail.

  • https://github.com/cassou/VL53L0X_rasp is pretty much a straight wrap of the STSW-IMG005 ST VL53L0X sensor driver library for Raspberry Pi. Note that the VL53L0X sensor is much simpler than the VL53L3CX, it can only track the distance of a single object, whereas the VL53L3CX reports more of a statistical dispersion to be quantitatively analysed.

  • https://github.com/pimoroni/VL53L0X-python is a Raspberry Pi Python library for the VL53L0X sensor, essentially a higher level convenience wrap into Python of the preceding library. Enterprising readers may wish to consider forking their repo and enhancing it to support this repo.

Build instructions

I personally find the Raspberry Pi Zero fast enough to build on device (takes a minute or two):

git clone https://github.com/ned14/VL53L3CX_rasppi.git
cd VL53L3CX_rasppi
make

The ST original distro comes with an example program to demonstrate the sensor in action. I have hack-ported this program to Raspberry Pi. Assuming that your sensor is connected as follows:

  • VDD on sensor is left unconnected.
  • VIN on sensor to pin 1 3V3 on Raspberry Pi.
  • GND on sensor to pin 9 GND on Raspberry Pi.
  • SDA on sensor to pin 3 SDA on Raspberry Pi.
  • SCL on sensor to pin 5 SCL on Raspberry Pi.
  • XSHUT on sensor is left unconnected (it defaults pull up to VDD i.e. sensor is enabled).
  • GPIO1 on sensor is left unconnected (this goes high when a new measurement is ready).

To build and run this example:

make example
bin/main

The demonstration program will print output such as:

Count=  226, #Objs=2 status=7, D=  713mm, S=1734144mm, Signal=0.09 Mcps, Ambient=0.20 Mcps
                     status=0, D= 1685mm, S= 686080mm, Signal=0.34 Mcps, Ambient=0.20 Mcps
Count=  227, #Objs=2 status=0, D=  720mm, S=2694144mm, Signal=0.04 Mcps, Ambient=0.21 Mcps
                     status=0, D= 1715mm, S= 764928mm, Signal=0.31 Mcps, Ambient=0.21 Mcps

This is actually of my head from the end of my arm, it gives the distances of my head and of the wall behind my head. The VL53L3CX can distinguish between up to four objects in its field of view at a time, and it also provides information on what it can see:

  • status is zero when the reading is valid. Other values usually mean reading is flawed somehow (e.g. 12 is when the sensor knows there is something within distance, but the SnR is too low to estimate distance reliably. You can find a table of possible values in the docs).

  • S is a measure of the standard deviation of the estimated distance (sigma). Note the large uncertainty above on my head above as compared to the wall behind me (my head will scatter a lot of laser, and my hair won't reflect laser as uniformly as paint on the wall).

  • Signal is a measure of how reflective-big the object is.

  • Ambient is a measure of the ambient light. Note this is a very high quality low infrared measure useful for outdoor heat estimation e.g. sunshine.

These are the results from statistically analysing the raw histogram returned by the sensor which is essentially how many laser photons were reflected back to the sensor over time after emission. Signal is therefore a function of both size and reflectivity to 940 nm light of the object at a given distance, and whilst one cannot disambiguate between size and reflectivity, if one knows that human skin has a reflectivity to 940 nm light of about 0.5, then one can detect a face sized patch of human skin, albeit that double the area of a material with 0.25 reflectivity would read exactly the same at the same distance, and only what enters the sensor's field of view is counted.

One of the really great things about the VL53L3CX sensor over any other distance sensor is that one can take a measure of the size of the object in the field of view. For example, if pointing at a bed from directly above, this sensor can tell the difference between someone standing near the bed (two objects one very near one very far), sitting on the bed (three objects: head, legs, bed), or lying on the bed (two objects, higher within half a metre of bed). It can also differentiate between two people in the bed (Signal doubles), or a child in the bed (Signal halves), and depending on the reflectivity of the duvet, whether the duvet is drawn over a person lying down or not. This, combined with its ability to make thousands of estimations per second, makes it unique amongst distance sensors in this price range. Indeed, put it on top of a spinning motor and you've got yourself a LIDAR!

Enjoy your sensor!