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Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 meter accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown., Published in the IEEE Robotics and Automation Letters and presented at the IEEE 2021 International Conference on Robotics and Automation. See https://www.youtube.com/watch?v=_qwAoYK9iGU for accompanying video

Teachers are increasingly recognizing the need—and their power—to advocate on behalf of the teaching profession for the sake of their students and communities. National organizations like AAPT have a key role in amplifying teachers’ voices. Building on the strategic vision for K-12 physics teacher professional development of the “Aspiring to Lead” report (Fig. 1), AAPT broke new ground by supporting teams of educators in addressing local- and state-level issues related to physics education.

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions.

This paper presents the thermal design of actuators in the perching arm of Astrobee robot that will operate inside the International Space Station (ISS) in starting 2019. Since the crew's safety is of the utmost importance on the ISS, all materials used in the Astrobee robot should meet the touch temperature requirements according to the ISS safety standards to protect crew from skin burns. The Astrobee perching arm consists of 2-DOF arm servo motors and 1- DOF gripper DC motor, which are capable of overheating when stalled, particularly given the lack of gravity-driven thermal convection in the ISS zero-gee environment. Thermal properties of two types of actuators are verified by monitoring the touch temperature in worst-case operations with no thermal protection. Then, the proper thermal protection designs have been conducted and installed to guarantee the safety in all conditions.

HTC Vive has been gaining attention as a cost-effective, off-the-shelf tracking system for collecting ground truth pose data. We assess this system's pose estimation through a series of controlled experiments where we show its precision to be in the millimeter magnitude and accuracy to range from millimeter to meter. We also show that Vive gives greater weight to inertial measurements in order to produce a smooth trajectory for virtual reality applications. Hence, the Vive's off the shelf algorithm is poorly suited for robotics applications such as measuring ground truth poses, where accuracy and repeatability are key. Therefore we introduce a new open-source tracking algorithm and calibration procedure for Vive which address these problems. We also show that our approach improves the pose estimation repeatability and accuracy by up to two orders of magnitude.