ACEINNA releases video: 'How to Develop Localization & Navigation Sensor Solutions for Drones, Autonomous Vehicles and Robots' | Automation.com

ACEINNA releases video: 'How to Develop Localization & Navigation Sensor Solutions for Drones, Autonomous Vehicles and Robots'

ACEINNA releases video: 'How to Develop Localization & Navigation Sensor Solutions for Drones, Autonomous Vehicles and Robots'
This short video provides an overview of ACEINNA’s new OpenIMU package.



January 16, 2019 - ACEINNA announced a new video on the ACEINNA YouTube Video Channel – “All About the New ACEINNA OpenIMU Package” at https://www.youtube.com/watch?v=NmZcIOO7fyw&t=3s

This short video provides an overview of ACEINNA’s new OpenIMU package.

ACEINNA’s OpenIMU solution consists of three key parts.

First is a family of Inertial Measurement Units consisting of three high-accuracy accelerometers, three high-accuracy gyros, and a powerful ARM Coretex. Zoom in on OpenIMU and OpenIMU CAN Second is an OpenSource tool chain and reference code for programming the IMU. It consists of everything from basic download and debug to reference implementations of loosely-coupled GPS/INS. Third is a full Developer Site and tools with charting, graphing and even algorithm simulation.

The OpenIMU Development hardware development kit includes JTAG-pod, precision mount fixture, EVB, and an OpenIMU300 module.  The OpenIMU module features ACEINNA’s 5 deg/Hr, 9-Axis gyro, accelerometer, and magnetometer sensor suite with an onboard 180MHz ARM Coretex floating-point CPU.   The IMU is delivered in a 24x37x9.5mm module that operates from 2.7-5.5VDC.

This freely downloadable stack includes:

* FreeRTOS-based data collection and sampling engine

* Performance-tuned, real-time, navigation-grade GPS/INS Kalman Filter library

* Free IDE/compiler tool chain based on Visual Studio Code

* JTAG debugging for debugging code loaded on IMU

* Data logging, graphing, Allen Variance plots, and maps,

* Extensive documentation

* Simulation environment with advanced sensor error models

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