Dueño de clínica Ola deberá pagar ₡13 millones tras perder juicio contra distribuidora de implantes
Tribunal descartó supuesta propaganda desleal al no acreditarse los hechos denunciados por el centro médico.
La entrada Dueño de clínica Ola deberá pagar ₡13 millones tras perder juicio contra distrib [...]
#AlbertoGarcía #Cirugías #ClinicaOla #DanielSoley #Demanda #GerardoHuertas #HerramientasMédicas #Implantes #MarcoChacón #País #RedesSociales #RonaldTorres #ÚltimaHora
Our paper has been accepted at #ICRA2023 🥳🍾
London, here we go!! @jm__guerrero
#AlbertoGarcia @FranciscoLera @vmatellan
@IntellRobotLabs
@RoboticaUnileon @URJCcientifica
Paper: https://arxiv.org/abs/2209.07586
Code (#ROS2/#Nav2): https://github.com/fmrico/mh_amcl
Video: https://youtu.be/LnmQ11Ew01g
Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new location that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the sytems's output. As novelties, our work includes a multi-scale match matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state-of-the-art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive ACML solution, obtaining good accuracy and recovery times.
Semanario Universidad
Francisco Martín Rico