746 research outputs found
Implementation of Collaborative RF Localization Using a Software-Defined Radio Network
This thesis investigates the use of collaboration between sensor nodes that were tasked with localizing a radio frequency emitter. Localization is a necessary component for dynamic spectrum access. Using a set of software-defined radios as our sensors and a received signal strength-based maximum likelihood localization algorithm, we successfully localized transmitting nodes based on their received signal strength. Our experiment was conducted outdoors using a flexible topology that could be shaped into 21 sub-topologies that varied in size, and orientation with respect to the transmitters. This was made possible through application of a time shift concept and a post-processing technique. We were able to compare our real world results with the simulated results of the same topologies. Although our simulation results did not fully comply with our real world results, we observed some common trends regarding effective topology design
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Multisensor Avionics Architecture for BVLOS Drone Services
This ADACORSA demonstrator focuses on the implementation of a failoperational
avionics architecture combining Commercial Off-The-Shelf (COTS) elements from
the automotive, the aerospace and the artificial intelligence world. A collaborative sensor
setup (Time-of-Flight camera and FMCW RADAR from Infineon Technologies, stereo camera,
LiDAR, IMU and GPS) allows to test heterogeneous sensor fusion solutions. A Tricore
Architecture on AURIXTM Microcontroller supports the execution of safety supervision tasks as
well as data fusion. A powerful embedded computer platform (NVIDIA Jetson Nano) accelerates
AI algorithms performance and data processing. Furthermore, an FPGA enables power
optimization of Artificial Neural Networks. Finally, a Pixhawk open-source flight controller
ensures stabilization during normal flight operation and provides computer vision software
modules allowing further processing of the captured, filtered and optimized environmental data.
This paper shows various hardware and software implementations highlighting their emerging
application within BVLOS drone services.EU-funded project ADACORSAECSEL Joint Undertaking (JU) under grant agreement No 876019European Union’s Horizon 2020German Federal Ministry of Education and Researc
GNSS-only Collaborative Positioning Among Connected Vehicles
Cooperative positioning is considered a key strategy for the improvement of localization and navigation performance in harsh contexts such as urban areas. Modern communication paradigms can support the exchange of inter-vehicle ranges measured from on-board sensors or obtained through Global Satellite Navigation System (GNSS) measurements. The paper presents an overview of the GNSS-only collaborative localization in the context of cooperative connected cars. It provides an experimental example along with new results about the tight integration of collaboratively-generated inter-vehicle relative measurements collected by a target vehicle by means of a double differentiation w.r.t. to a set of five aiding vehicles. An average improvement of the positioning accuracy of about 11% motivates the research effort towards multi-agent connected positioning systems
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
External multi-modal imaging sensor calibration for sensor fusion: A review
Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, InnovaciĂłn y Universidades | Ref. PID2019-108816RB-I0
- …