Real-Time Robotics: The Extended Kalman Filter
In this last part, we’ll explain how an Extended Kalman Filter works, and which libraries allow you to implement one in robotics.
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Our Expertise in Real-time Motion Control
Our OROCOS development work of the Real-Time Toolkit (RTT) gave us the leading expertise in Realtime Motion Control for both ROS and OROCOS. From grabbing satellites in zero-gravity to driving mobile robots, time-reliability is key!
- Frameworks
- OROCOS RTT · ROS · ROS 2
- Control
- Position · Velocity · Force
- Planning
- Reactive · Cartesian · Free-space
Capabilities
Control frameworks
- Integration and customization of the Open Robot Control Software (OROCOS) Framework - Real-Time Toolkit (RTT)
- Integration and customization of the ROS 2 Real-Time RMW implementation
- Creation of custom controllers including position, velocity, acceleration and force controllers
- Integration of real-time controllers in the ROS (Control) framework
- Implementation of real-time Kalman Filters and Extended Kalman Filters
Reactive planning
- Fast planners that are able to react and adjust to a changing environment
- Dual mode Cartesian and free-space planning algorithms for a wide range of applications
- System wide performance benchmarking
Learn more from our blog
Real-Time Robotics: How a Kalman Filter Can (not) Help
In this Part 2 of our mini series, we'll explore the Kalman Filter and if it can be used to enhance sensor quality and build real-time robotics applications.
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Real-Time Robotics: Before you start
Robotics systems rely heavily on sensors to provide accurate information about the environment and the robot itself. However, sensors are not perfect and often suffer from inaccuracies due to various factors. In this first Part, we dive into getting the basics right.
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