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1. A Deep Learning Approach to Localization for Navigation on a Miniature Autonomous Blimp

   https://doi.org/10.1109/ICCA51439.2020.9264514  

   Seguin, Landan; Zheng, Justin; Li, Alberto; Tao, Qiuyang; Zhang, Fumin (October 2020, IEEE International Conference on Control and Automation (ICCA))

   null (Ed.)

   The Georgia Tech Miniature Autonomous Blimp (GT-MAB) needs localization algorithms to navigate to way-points in an indoor environment without leveraging an external motion capture system. Indoor aerial robots often require a motion capture system for localization or employ simultaneous localization and mapping (SLAM) algorithms for navigation. The proposed strategy for GT-MAB localization can be accomplished using lightweight sensors on a weight-constrained platform like the GT-MAB. We train an end-to-end convolutional neural network (CNN) that predicts the horizontal position and heading of the GT-MAB using video collected by an onboard monocular RGB camera. On the other hand, the height of the GT-MAB is estimated from measurements through a time-of-flight (ToF) single-beam laser sensor. The monocular camera and the single-beam laser sensor are sufficient for the localization algorithm to localize the GT-MAB in real time, achieving the averaged 3D positioning errors to be less than 20 cm, and the averaged heading errors to be less than 3 degrees. With the accuracy of our proposed localization method, we are able to use simple proportional-integral-derivative controllers to control the GT-MAB for waypoint navigation. Experimental results on the waypoint following are provided, which demonstrates the use of a CNN as the primary localization method for estimating the pose of an indoor robot that successfully enables navigation to specified waypoints. 

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2. Simple Camera-to-2D-LiDAR Calibration Method for General Use

   https://doi.org/10.1007/978-3-030-64559-5_15  

   Palmer, Andrew H.; Peterson, Chris; Blankenburg, Janelle; Feil-Seifer, David; Nicolescu, Monica (October 2020, International Symposium Advances in Visual Computing (ISVC))

   As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low-cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of using a calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has led us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle. 

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3. Simple Camera-to-2D-LiDAR Calibration Method for General Use

   Palmer, A.; Peterson, C.; Blankenburg, J.; Feil-Seifer, D.; Nicolescu, M. (October 2020, ISVC'20: 15TH INTERNATIONAL SYMPOSIUM ON VISUAL COMPUTING)

   null (Ed.)

   As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has lead us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle. 

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4. Monocular Camera and Single-Beam Sonar-Based Underwater Collision-Free Navigation with Domain Randomization

   Yang, P.; Liu, H.; Roznere, M.; Quattrini Li, A. (January 2022, International Symposium on Robotics Research (ISRR))

   Billard, A.; Asfour, T.; Khatib, O. (Ed.)

   Underwater navigation presents several challenges, including unstructured unknown environments, lack of reliable localization systems (e.g., GPS), and poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation system, based on a monocular camera and a fixed single-beam echo-sounder, that efficiently navigates an underwater robot to waypoints while avoiding nearby obstacles. Our proposed method is based on Proximal Policy Optimization (PPO), which takes as input current relative goal information, estimated depth images, echo-sounder readings, and previous executed actions, and outputs 3D robot actions in a normalized scale. End-to-end training was done in simulation, where we adopted domain randomization (varying underwater conditions and visibility) to learn a robust policy against noise and changes in visibility conditions. The experiments in simulation and real-world demonstrated that our proposed method is successful and resilient in navigating a low-cost underwater robot in unknown underwater environments. The implementation is made publicly available at https://github.com/dartmouthrobotics/deeprl-uw-robot-navigation. 

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5. Toward Calibration of the Global Network of Gravitational Wave Detectors with Sub-Percent Absolute and Relative Accuracy

   https://doi.org/10.3390/galaxies10020042  

   Karki, Sudarshan; Bhattacharjee, Dripta; Savage, Richard L. (April 2022, Galaxies)

   The detection of gravitational-wave signals by the LIGO and Virgo observatories during the past few years has ushered us into the era of gravitational-wave astronomy, shifting our focus from detection to source parameter estimation. This has imposed stringent requirements on calibration in order to maximize the astrophysical information extracted from these detected signals. Current detectors rely on photon radiation pressure from auxiliary lasers to achieve required calibration accuracy. These photon calibrators have made significant improvements over the last few years, realizing fiducials displacements with sub-percent accuracy. This achieved accuracy is directly dependent on the laser power calibration. For the next observing campaign, scheduled to begin at the end of 2022, a new scheme is being implemented to achieve improved laser power calibration accuracy for all of the GW detectors in the global network. It is expected to significantly improve absolute and relative calibration accuracy for the entire network. 

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