VIO in dark environments
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Dear ModalAI-Team,
we are currently working on a drone project focused on inspecting dark or poorly lit environments. Therefore, we would like to know whether it is possible to achieve VIO (Visual-Inertial Odometry) with onboard lighting mounted on the drone. As far as we understand, VIO relies solely on the forward-facing tracking camera, is that correct? Or do downward-facing cameras (like on the Starling platform) also play a role in VIO? If only the front-facing camera is used for VIO, then a forward-facing light source should theoretically be sufficient, allowing us to avoid the complexity of downward illumination.
Additionally, we would like to know if a Time-of-Flight (ToF) sensor can be used to support VIO, which would obviously be beneficial in dark environments.Thank you very much in advance!
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Dear ModalAI-Team,
we are currently working on a drone project focused on inspecting dark or poorly lit environments. Therefore, we would like to know whether it is possible to achieve VIO (Visual-Inertial Odometry) with onboard lighting mounted on the drone. As far as we understand, VIO relies solely on the forward-facing tracking camera, is that correct? Or do downward-facing cameras (like on the Starling platform) also play a role in VIO? If only the front-facing camera is used for VIO, then a forward-facing light source should theoretically be sufficient, allowing us to avoid the complexity of downward illumination.
Additionally, we would like to know if a Time-of-Flight (ToF) sensor can be used to support VIO, which would obviously be beneficial in dark environments.Thank you very much in advance!
@Dronodev , if you are using QVIO, which only supports a single camera, then you would address that use case by providing illumination for the single camera.
Our latest work with open-vins has shown results with triple camera vio using Starling 2 : https://www.youtube.com/watch?v=12DWDV7XCiE , so providing 360 degree illumination should help with that.
The images are normalized to help extract features in dark regions. However, illuminating the environment should help. Please keep in mind several points:
- adding light to the drone can result in shadows that move together with the vehicle. The shadows can be coming from drone components like landing gear, etc.
- you could use IR light and if the lens does not have the IR filter, the camera will see the IR light without it actually being visible to the human eye. The IR filter is typically installed inside the lens (right between the inner part of the lens before the light exits from the lens and goes into the camera sensor). For experimentation, the lens can be unscrewed from the camera and the IR filter carefully removed).
Our AR0144 tracking cameras have lens option with or without IR filter : https://www.modalai.com/products/msu-m0166
Alex
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@Dronodev , if you are using QVIO, which only supports a single camera, then you would address that use case by providing illumination for the single camera.
Our latest work with open-vins has shown results with triple camera vio using Starling 2 : https://www.youtube.com/watch?v=12DWDV7XCiE , so providing 360 degree illumination should help with that.
The images are normalized to help extract features in dark regions. However, illuminating the environment should help. Please keep in mind several points:
- adding light to the drone can result in shadows that move together with the vehicle. The shadows can be coming from drone components like landing gear, etc.
- you could use IR light and if the lens does not have the IR filter, the camera will see the IR light without it actually being visible to the human eye. The IR filter is typically installed inside the lens (right between the inner part of the lens before the light exits from the lens and goes into the camera sensor). For experimentation, the lens can be unscrewed from the camera and the IR filter carefully removed).
Our AR0144 tracking cameras have lens option with or without IR filter : https://www.modalai.com/products/msu-m0166
Alex
@Alex-Kushleyev Thank you very much!
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