About Klaus Petersen

I like to create magical things, especially projects related to new technologies like augmented and virtual reality, mobile robotics and MEMS-based sensor networks. I code in C(++) and Python, trying to keep up with my very talented colleagues :-)

Collaboration with Omni Instruments Ltd.

We are happy to announce that we are starting a collaboration with Omni Instruments Ltd. in the UK. Omni Instruments Ltd. is developer and distributor of high definition measurement and logging systems. We have started the collaboration with offering two versions of the LPMS sensor system under the Omni Instruments brand. Please see the below links for further information:

LPMS-CU 9-Axis IMU AHRS Motion Sensor with CANbus and USB Connectivity
LPMS-B 9-Axis IMU AHRS Motion Sensor with Bluetooth Connectivity

LPMS-B and LPMS-CU OEM Versions

We also offer a so-called OEM version of our sensors. That means a bare bone version of the sensor without case and (in the case of LPMS-B) battery. We recommend buying a full development kit for testing of the sensor for first-time customers. However if you intend to integrate the sensor into your special design, the reduced space requirements of the OEM version might be very attractive.

Additionally to connectivity provided by the daughterboard, the LPMS-CU and LPMS-B mainboard can communicate by RS-232 (TTL levels). The RS-232 levels can be accessed through the SMD connector (as shown below) between sensor mainboard and communication daughterboard. Please contact us, if you need further information about this connector.

LpGlass and Head Tracking Revisited

We had the opportunity to try out one of the new augmented reality glasses AiRScouter produced by the Japanese company Brother. We first tried one at a Brother product exhibition here in Tokyo. Although the glasses are a little heavier than normal glasses, they fit quite well and the overlay image is well visible.

LP-Glass

We experimented with the glasses a bit and set-up a prototype application for augmented reality using our LPMS-B sensor for head tracking, codename: LpGlass. The video below shows a demo of our LPMS-B IMU attached to the AiRScouter.

Similar to the Google glasses there seem to be a huge number of applications, especially for augmenting task environments for medical procedures, industrial assembly, education etc.

LPMS-CU Rugged

So far we have offered our customers only one packing option for the LPMS-CU, our standard blue plastic casing. The plastic case is small, very light and fairly robust. However, in harsh environments or in places that engineers regularly access with larger tools, we thought that a more rugged case for the LPMS-CU might be desireable. Therefore we have designed a new Aluminium casing option for LPMS-CU: the LPMS-CU-Rugged. Customers can from now on order this casing as an option when purchasing the LPMS-CU. The case is slightly larger and heavier than the plastic case, but made from 2mm Aluminium, it is almost indestructable.

Visualization of Magnetic Field Calibration Data

One of the trickiest things for reliably measuring orientations with the LPMS is the calibration of the magnetic field sensor. The functionality of the sensor is essential for determining the yaw angle of the sensor without drift. If we used only the gyrsocope to measure the yaw angle a drift of a few angles would already occur after 10 or 20 seconds of movement.

The normally spherical shape of the environment magnetic field is, especially in the vicinity of metal or electric circuits, often distorted to an ellipsoid. Such distoritions are efficiently compensated by calibrating the LPMS. However it is hard for the user to see if the calibration was successful or what the resulting data means about the surrounding electromagnetic field. Therefore we added a visualization of this data to the control software of the sensor (LpmsControl) that is to give a better understanding of the calibration results (see image below).

We use a special algorithm to reduce the influence of a distorted magnetic environment field on the orientation measurements of the sensor. A comparison of orientation tracking without and with using this algorithm is shown below.

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