Autonomous robotics is a rapidly-growing field and of increasing importance to undergraduate engineering programs. As technology advances, robots are moving past basic repetitive jobs and finding uses that further expand human capabilities. They are becoming more capable of taking over dangerous tasks, as well as freeing humans up for more meaningful work. This progress requires robots to be able to sense their environment and act without supervision.Toward this end, we are addressing a major problem in autonomous robotics, which is simultaneous localization and mapping (SLAM). SLAM is still very much in its research and development phase. Currently there are many methods available which to varying degrees solve the localization problem of SLAM. The majority use probabilistic concepts. However, these solutions often bring with them a high level of algorithmic complexity. This can raise the cost of implementation and make fundamental concepts difficult to comprehend, which is especially a problem for engineering students being exposed to SLAM principles for the first time. Furthermore, because high complexity may not be necessary when the constraints of time and accuracy are not as severe, some applications do not require the rigor of a full probabilistic SLAM method. Therefore, a solution to the localization problem which focuses on clarity and first principles can be beneficial to the engineering/education community and for applications where low cost is favored over high performance.The aim of our project is to develop a practical and relatively simple robotic development platform as a starting point for future research and development. The platform and research results will also be incorporated into a laboratory course covering the fundamentals of localization. With that in mine, the platform is constructed with low cost off the shelf components which are both accessible and of common use in the engineering education community. Therefore, we present a straightforward approach to the localization portion of the SLAM problem scan matching techniques. By reducing complex scan matching from a multi-dimensional to a one-dimensional problem, localization can be solved in an easily understandable way while minimizing cost and computational requirements. The solution presented here provides estimates of the robot pose and orientation within the confines of a static and simple environment. The techniques utilized are, cross correlation calculations for pure rotation movements around the robot’s center axis combined with simple root mean squared error calculations for translation movements. The result is a simple robot able to navigate while maintaining pose and orientation.

Fundamental Scan Matching Approach to Lidar Based Localization

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.