Virtual Physical Reality With Kintinuous And An Oculus Rift


The Kinect has long been able to create realistic 3D models of real, physical spaces. Combining these Kinect-mapped spaces with an Oculus Rift is something brand new entirely.

[Thomas] and his fellow compatriots within the Kintinuous project are modeling an office space with the old XBox 360 Kinect’s RGB+D sensors. then using an Oculus Rift to inhabit that space. They’re not using the internal IMU in the Oculus to position the camera in the virtual space, either: they’re using live depth sensing from the Kinect to feed the Rift screens.

While Kintinuous is very, very good at mapping large-scale spaces, the software itself if locked up behind some copyright concerns the authors and devs don’t have control over. This doesn’t mean the techniques behind Kintinuous are locked up, however: anyone is free to read the papers (here’s one, and another, PDF of course) and re-implement Kintinuous as an open source project. That’s something that would be really cool, and we’d encourage anyone with a bit of experience with point clouds to give it a shot.

Video below.

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Charlotte, the hexapod with 3D vision


Charlotte’s chassis comes from as a kit, but the stock electronics are based on an Arduino – not something for a robot that needs to run computer vision apps. [Kevin] ended up using a Raspi for the controller and gave Charlotte eyes with an Asus XTION. Edit: or a PrimeSense sensor These sensors are structured light depth cameras just like the kinect, only about smaller, lighter, and have a better color output.

Hardware is only one half of the equation, so [Kevin] tossed the Arduino-based stock electronics and replaced them with a Raspberry Pi. This allowed him to hone his C++ skills and add one very cool peripheral – the XTION depth camera.

To the surprise of many, we’re sure, [Kevin] is running OpenNI on his Raspberry Pi, allowing Charlotte to take readings from her depth camera and keep from colliding into any objects. The Raspberry Pi is overclocked, of course, and the CPU usage is hovering around 90%, but if you’re looking for a project that uses a depth sensor with a Pi, there you go.

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