SI-01

(Oral presentation, session Smart Industry, 13.50 - 14.10 hrs)

Localization and mapping are important techniques that allow robots to autonomously navigate through a building. SLAM is the abbreviation for Simultaneous Localization and Mapping. Localization is the problem of answering the question: “Where am I?” Mapping is the problem of integrating the information gathered with the robot's sensors. It is the problem of answering the question “What does the world look like?”

SLAM is defined as the problem of building a map while at the same time localizing the robot within that map. In practice, these two problems cannot be solved independently of each other. Before a robot can answer the question of what the environment looks like given a set of observations, it needs to know from which locations these observations have been made. At the same time, it is hard to estimate the current position of a vehicle without a map. Therefore, SLAM is often referred to as a chicken and egg problem: A good map is needed for localization while an accurate pose estimate is needed to build a map.

Although the problems stated above might seem like trivial problems, because humans solve them all the time, they are challenging for robots. Especially when applied in an industrial situation, where the circumstances like dust and dirt influence the sensor readings.

The Mechatronics research group of Saxion researches the applicability of state of the art SLAM algorithms and commercially available hardware. These techniques are applied in autonomous vehicles in Smart Industry (TFF SLAMming project) and Agriculture (Modular Adaptive Robot for Intelligent Operations, TFF MARIO project). This research opens up the possibilities of autonomous navigation to SMEs.