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Abstract. Robot navigation is one of the most studied problems in robotics and the key capability for robot autonomy. Navigation techniques have become more and more reliable, but evaluation mainly focused on individual navigation components (i.e., mapping, localization, and planning) using datasets or simulations. The goal of this paper is to define an experimental protocol to evaluate the whole navigation system, deployed in a real environment. To ensure repeatability and reproducibility of experiments, our benchmark protocol provides detailed definitions and controls the environment dynamics. We define standardized environments and introduce the concept of a reference robot to allow comparison between different navigation systems at different experimentation sites. We present applications of our protocol in experiments in two different research groups, showing the usefulness of the benchmark.

Practical mapping and navigation solutions for large indoor environments continue to rely on relatively expensive range scanners, because of their accuracy, range and field of view. Microsoft Kinect on the other hand is inexpensive, is easy to use and has high resolution, but suffers from high noise, shorter range and a limiting field of view. We present a mapping and navigation system that uses the Microsoft Kinect sensor as the sole source of range data and achieves performance comparable to state-of-theart LIDAR-based systems. We show how we circumvent the main limitations of Kinect to generate usable 2D maps of relatively large spaces and to enable robust navigation in changing and dynamic environments. We use the Benchmark for Robotic Indoor Navigation (BRIN) to quantify and validate the performance of our system.

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People enjoy listening to music as part of their life. This makes music an excellent choice for designing a user-friendly brain-computer interface (BCI) for long-term use. We propose a novel BCI system using music stimuli that relies on brain signals collected via Smartfones, an EEG recording device integrated into a pair of headphones. In a user study of the proposed system, participants were asked to pay attention to one of three musical instruments playing simultaneously from separate spatial directions. We used a stimulus reconstruction method to decode attention from EEG signals. Results show that the proposed system can achieve good decoding accuracy (>70%) while providing superior user-friendliness compared to a traditional EEG setup.