The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster.

Nowadays, companies are demanding better‐prepared professionals to succeed in a digital society, and the acquisition of Science, Technology, Engineering, Arts, and Mathematics (STEAM)‐related competencies is a key issue. One of the main problems in this sense is how to integrate STEAM into the current educational curricula. This is not something related to a subject or educational trend but rather to new methodological approaches that can engage students. In this sense, such active methodologies that apply mechatronics and robotics could be an interesting path to pursue. Given this context, the first necessary task in evaluating the potential of this approach is to understand the landscape of the application of robotics and mechatronics in STEAM Education and how active methodologies are applied in this sense. To carry out this analysis in a systematic and replicable manner, it is necessary to follow a methodology. In this case, the researchers employ a systematic mapping review. This paper presents this process and its main findings. Fifty‐four studies have been selected out of 242 total studies analyzed. From these, beyond obtaining a clear vision of the STEAM landscape regarding project topics, we can also conclude that robotics and physical devices have been applied successfully with collaborative methodologies in STEAM Education. Regarding conclusions, this paper shows that robotics and mechatronics applied with active methodologies is to be a good means to engage students in STEAM disciplines and thus aid the acquisition of what is commonly known as “21st‐century skills.”

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