Mihai Jalobeanu

An Experimental Protocol for Benchmarking Robotic Indoor Navigation

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.

Reliable kinect-based navigation in large indoor environments

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-the-art 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.

Rapid development of multimodal interactive systems: a demonstration of platform for situated intelligence

We demonstrate an open, extensible platform for developing and studying multimodal, integrative-AI systems. The platform provides a time-aware, stream-based programming model for parallel coordinated computation, a set of tools for data visualization, processing, and learning, and an ecosystem of pluggable AI components. The demonstration will showcase three applications built on this platform and highlight how the platform can significantly accelerate development and research in multimodal interactive systems.

An inexpensive method for evaluating the localization performance of a mobile robot navigation system

We propose a method for evaluating the localization accuracy of an indoor navigation system in arbitrarily large environments. Instead of using externally mounted sensors, as required by most ground-truth systems, our approach involves mounting only landmarks consisting of distinct patterns printed on inexpensive foam boards. A pose estimation algorithm computes the pose of the robot with respect to the landmark using the image obtained by an on-board camera. We demonstrate that such an approach is capable of providing accurate estimates of a mobile robots position and orientation with respect to the landmarks in arbitrarily-sized environments over arbitrarily-long trials. Furthermore, because the approach involves minimal outfitting of the environment, we show that only a small amount of setup time is needed to apply the method to a new environment. Experiments involving a state-of-the-art navigation system demonstrate the ability of the method to facilitate accurate localization measurements over arbitrarily long periods of time.