FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

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1. Proche Pas pertinent

   Infrastructure Node-based Vehicle Localization for Autonomous Driving

   Elijah S. Lee, Ankit Vora, Armin Parchami et al. · 2021 · Robotique · Résumé IA disponible

   Résumé scientifique

   Vehicle localization is essential for autonomous vehicle (AV) navigation and Advanced Driver Assistance Systems (ADAS). Accurate vehicle localization is often achieved via expensive inertial navigation systems or by employing compute-intensive vision processing (LiDAR/camera) to augment the low-cost and noisy inertial sensors. Here we have developed a framework for fusing the information obtained from a smart infrastructure node (ix-node) with the autonomous vehicles on-board localization engine to estimate the robust and accurate pose of the ego-vehicle even with cheap inertial sensors. A smart ix-node is typically used to augment the perception capability of an autonomous vehicle, especial…

2. Proche Pas pertinent

   Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments

   Shibo Zhao, Hengrui Zhang, Peng Wang et al. · 2021 · Robotique · Résumé IA disponible

   Résumé scientifique

   We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments. Different from traditional sensor-fusion methods, Super Odometry employs an IMU-centric data processing pipeline, which combines the advantages of loosely coupled methods with tightly coupled methods and recovers motion in a coarse-to-fine manner. The proposed framework is composed of three parts: IMU odometry, visual-inertial odometry, and laser-inertial odometry. The visual-inertial odometry and laser-inertial odometry provide the pose p…

3. Proche Pas pertinent

   Ambient awareness for agricultural robotic vehicles

   Giulio Reina, Annalisa Milella, Raphael Rouveure et al. · 2021 · Biosystems Engineering 146 (2016) 114-132 · Résumé IA disponible

   Résumé scientifique

   In the last few years, robotic technology has been increasingly employed in agriculture to develop intelligent vehicles that can improve productivity and competitiveness. Accurate and robust environmental perception is a critical requirement to address unsolved issues including safe interaction with field workers and animals, obstacle detection in controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness, in general, towards increased process automation. Given the variety of conditions thatmay be encountered in the field, no single sensor exists that can guarantee reliable results in every scenario. The development of a multi-sensor…

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   Lidar SLAM for Autonomous Driving Vehicles

   Farhad Aghili · 2022 · Robotique · Résumé IA disponible

   Résumé scientifique

   This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SL…

5. Apparenté Pas pertinent

   SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

   Yuezhan Tao, Yuwei Wu, Beiming Li et al. · 2022 · Robotique · Résumé IA disponible

   Résumé scientifique

   We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher succ…

6. Apparenté Pas pertinent

   UrbanFly: Uncertainty-Aware Planning for Navigation Amongst High-Rises with Monocular Visual-Inertial SLAM Maps

   Ayyappa Swamy Thatavarthy, Sudarshan S Harithas, Gurkirat Singh et al. · 2022 · Robotique · Résumé IA disponible

   Résumé scientifique

   We present UrbanFly: an uncertainty-aware real-time planning framework for quadrotor navigation in urban high-rise environments. A core aspect of UrbanFly is its ability to robustly plan directly on the sparse point clouds generated by a Monocular Visual Inertial SLAM (VINS) backend. It achieves this by using the sparse point clouds to build an uncertainty-integrated cuboid representation of the environment through a data-driven monocular plane segmentation network. Our chosen world model provides faster distance queries than the more common voxel-grid representation, and UrbanFly leverages this capability in two different ways leading to as many trajectory optimizers. The first optimizer us…

7. Apparenté Pas pertinent

   Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry

   Yuyang Chen, Dingkang Wang, Lenworth Thomas et al. · 2023 · IEEE Transactions on Robotics, 2024. · Résumé IA disponible

   Résumé scientifique

   A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of…

8. Apparenté Pas pertinent

   Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments

   Riccardo Polvara, Francesco Del Duchetto, Gerhard Neumann et al. · 2021 · Robotique · Résumé IA disponible

   Résumé scientifique

   The agricultural domain offers a working environment where many human laborers are nowadays employed to maintain or harvest crops, with huge potential for productivity gains through the introduction of robotic automation. Detecting and localizing humans reliably and accurately in such an environment, however, is a prerequisite to many services offered by fleets of mobile robots collaborating with human workers. Consequently, in this paper, we expand on the concept of a topological particle filter (TPF) to accurately and individually localize and track workers in a farm environment, integrating information from heterogeneous sensors and combining local active sensing (exploiting a robot's onb…