1,721,183 research outputs found
An IMU/UWB/Vision-based Extended Kalman Filter for Mini-UAV Localization in Indoor Environment using 802.15.4a Wireless Sensor Network
Full text linkIndoor localization of mobile agents using wireless technologies is becoming very important in military and civil applications. This paper introduces an approach for the indoor localization of a mini UAV based on Ultra-WideBand technology, low cost IMU and vision based sensors. In this work an Extended Kalman Filter (EKF) is introduced as a possible technique to improve the localization. The proposed approach allows to use a low-cost Inertial Measurement Unit (IMU) in the prediction step and the integration of vision-odometry for the detection of markers nearness the touchdown area. The ranging measurements allow to reduce the errors of inertial sensors due to the limited performance of accelerometers and gyros. The obtained results show that an accuracy of 10 cm can be achieved
Embedded Multisensor System for Safe Point-to-Point Navigation of Impaired Users
No full textNew smart objects to improve the quality of life in the ambient assisted living (AAL) scenario are capturing the interest of researchers and companies. In particular, novel assistive technologies are being developed to make accessible street navigation to impaired people. The solution that we propose in this new application domain of intelligent transportation systems is a framework for a safe point-to-point navigation, owing to high-detailed road graphs, including sidewalks, crosswalks, and generic “obstacles.” The system is based on a low-cost modular sensor box (embedded hardware) interfaced with a mobile/phone application that acts as an intelligent navigator. The main novelty is the capability to sense the surrounding area while being able to perform a fast path replanning, owing to a real-time link to a remote server, if an obstacle is detected. The sensing is performed using different sensors, such as ultrasound, lidar, and a 77-GHz mid-range automotive radar (absolutely novel in the AAL context), which are processed and fused in the well-established robot operating system (ROS). We tested the framework by analyzing its performance in two different configurations and environments by using, respectively, a sonar and a laser rangefinder in a building scenario and a radar in an urban environment. Even if in both cases results demonstrated a quite good robustness in the obstacle detection with a quasi-real-time route replanning, we were mainly interested and succeeded in demonstrating the high flexibility and extensibility of our framework
Improving Variable Rate Treatments by Integrating Aerial and Ground Remotely Sensed Data
No full textAutomatic Extraction of Urban Objects from Multi-Source Aerial Data
No full textToday, one of the main applications of multi-source aerial data is the city modelling. The capability to automatically detect objects of interest starting from LiDAR and multi-spectral data is a complex and an open problem. The information obtained can be also used for city planning, change detection, road graph update, land cover/use. In this paper we present an automatic approach to object extraction in urban area; the proposed approach is based on different sequential stages. The first stage basically solves a multi-class supervised pixel based classification problem (building, grass, land and tree) using a boosting algorithm; after classification, the next step provides to extract and filter land areas from classified data; the last step extracts roundabouts by the Hough transform and linear roads by a novel approach, which is robust to noise (sparse pixels); the final representation of extracted roads is a graph where each node represents a
cross between two or more roads. Results on a real dataset of Mannheim area (Germany) using both LiDAR (first - last pulses) and multi-spectral high resolution data (Red - Green - Blue - Near Infrared) are presented
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