For 30 years there has been intense research in the Simultaneous Localization and Mapping (SLAM) field of robotics, which is the process by which machines employ various sensors to concurrently map out their environment and ascertain their location. “SLAM is an essential building block of autonomous robots because robots, such as planetary rovers and undersea research craft, cannot be provided with an accurate map beforehand,” says Politecnico di Milano University roboticist Matteo Matteucci. “In such situations, the only solution is for them to create a representation of the environment as they go and determine their location in it by themselves.” Matteucci has led the Rawseeds project, a multi-university effort to create a novel set of free benchmarking tools so that fellow roboticists can compare SLAM strategies and algorithms against each other. The researchers launched the benchmarking process by developing a unique robotic test platform that incorporated six distinct vision, laser, and sonar sensor types, which they used to capture synchronized sensor data for SLAM. They then ran the platform in different indoor and outdoor environments, adjusting factors such as lighting conditions or the presence of people or moving objects. “Our goal was to establish a common, predefined way of measuring the performance of SLAM algorithms that differ by approach and sensors used–benchmarks that other algorithms could then be compared against,” Matteucci says. Future-generation robots could become smarter through the new benchmarks, he says.
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Tags: Robot
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