A LiDAR-Camera-Inertial-GNSS Apparatus for 3D Multimodal Dataset Collection in Woodland Scenarios.

Forestry operations have become of great importance for a sustainable environment in the past few decades due to the increasing toll induced by rural abandonment and climate change. Robotics presents a promising solution to this problem; however, gathering the necessary data for developing and testing algorithms can be challenging. This work proposes a portable multi-sensor apparatus to collect relevant data generated by several onboard sensors. The system incorporates Laser Imaging, Detection and Ranging (LiDAR), two stereo depth cameras and a dedicated inertial measurement unit (IMU) to obtain environmental data, which are coupled with an Android app that extracts Global Navigation Satellite System (GNSS) information from a cell phone. Acquired data can then be used for a myriad of perception-based applications, such as localization and mapping, flammable material identification, traversability analysis, path planning and/or semantic segmentation toward (semi-)automated forestry actuation. The modular architecture proposed is built on Robot Operating System (ROS) and Docker to facilitate data collection and the upgradability of the system. We validate the apparatus' effectiveness in collecting datasets and its flexibility by carrying out a case study for Simultaneous Localization and Mapping (SLAM) in a challenging woodland environment, thus allowing us to compare fundamentally different methods with the multimodal system proposed.

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.

Análisis espacial de las réplicas del terremoto de Arequipa del 23 de junio de 2001 a partir de datos de una red sísmica local : (Resultados preliminares)

En el presente estudio se realiza el análisis y la evaluación de la distribución espacial de las réplicas del terremoto de Arequipa ocurridas entre el 28 de junio y 19 de julio de 2001. Estas réplicas fueron registradas por una red sísmica local compuesta por siete estaciones digitales de tipo reftek que funcionaron por un periodo de 40 días, tiempo durante el cual ocurrieron hasta tres réplicas con magnitudes Mw de 6.5, 6.8 y 7.5. Los resultados indican que el área total de ruptura asociado al terremoto de Arequipa fue de 370x150 km² con el eje mayor paralelo a la línea de costa. La distribución de las réplicas, en profundidad, sugiere que la superficie involucrada en la ruptura presenta una inclinación de 27º. Asimismo, a partir del área de ruptura se estima un desplazamiento medio de 77 cm.(AU)

Análisis del terremoto del sur de Perú, 23 de junio de 2001, Mw=8.4 : Utilizando datos locales

Se analiza la secuencia de réplicas del mayor terremoto a nivel mundial de los últimos 25 años, que corresponde al ocurrido en el sur del Perú el 23 de junio de 2001, Mw=8.4, utilizando datos sismológicos obtenidos con redes locales de observación y monitoreo. Se discute la sismicidad histórica, principalmente lo relativo a la estimación de las magnitudes determinadas con parámetros macrosísmicos, considerando que dichas magnitudes no incluyen particularmente el efecto de sitio y la directividad del proceso de ruptura. Se concluye que el terremoto de 2001 es equivalente al de 1784 y no al de 1868, que es el mayor reportado en el sur del Perú. El área de réplicas determinada con datos de la red mundial y con observaciones locales indica que el segmento comprendido entre ilo y Tacna permanece intacto. Finalmente se indica que considerando la ocurrencia del terremoto de Antofagasta ocurrido en 1995 y el terremoto del sur de Perú de 2001, la probabilidad de ocurrencia de un sismo mayor en la brecha sísmica del norte de Chile ha aumentado, sin embargo dicho próximo evento no será necesariamente equivalente al último de la serie, vale decir al de 1877.(AU)