In a pioneering effort towards advancing the realm of mobile robot control systems, our partner, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, has recently made an important contribution to the scientific community. Their latest research article, titled “Energy Efficient RANSAC Algorithm for Flat Surface Detection in Point Clouds,” was published in the first volume of the Energy Engineering and Control Systems Journal in the first Volume of 2023 by Lviv Polytechnic National University .

Mobile robot control systems have witnessed remarkable progress, largely owing to robust environmental analysis algorithms. These algorithms, reliant on data gathered from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs), empower mobile robots to navigate their surroundings effectively. In particular, these sensors generate vital information about the objects in the robot’s environment in the form of point clouds. The primary function of such algorithms is to detect objects of interest, locate specified objects, and facilitate the robot’s autonomous positioning within its environment.

While various approaches have been explored to tackle object detection within point clouds, many of them demand extensive computational resources. In their research, UNIKIEV has meticulously scrutinized numerous iterations of the random sample consensus (RANSAC) algorithm, specifically for objects defined by mathematical models of analytical nature. Employing statistical data analysis, the study meticulously compares these RANSAC algorithm modifications. The results reveal a highly energy-efficient approach for the detection of flat surfaces, which impressively processes up to 60 RGB-D camera frames per second.

The detection of planes in depth images serves as a foundational aspect of all 3 use cases within the MASTERLY project. Recognizing the pivotal role of the RANSAC algorithm, UNIKIEV’s research has explored a range of algorithmic variations with a key focus on their energy efficiency. This research promises to significantly enhance the energy efficiency of mobile robot control systems, aligning seamlessly with the MASTERLY project’s objectives.

The full research article can be accessed at the this link or on Zenodo.