This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency ...
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Various training-based spatial filtering methods have been proposed to classify steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, many overlook the temporal instability of S...
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In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential re...
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The use of autonomous vehicles in maritime operations is a technological challenge. In the particular case of autonomous aerial vehicles (UAVs), their application ranges from inspection and surveillance of offshore po...
The use of autonomous vehicles in maritime operations is a technological challenge. In the particular case of autonomous aerial vehicles (UAVs), their application ranges from inspection and surveillance of offshore power plants, and marine life observation, to search and rescue missions. Manually landing UAVs onboard water vessels can be very challenging due to limited space onboard and wave agitation. This paper proposes an autonomous solution for the task of landing commercial multicopter UAVs with onboard cameras on water vessels, based on the detection of a custom landing platform with computer vision techniques. The autonomous landing behavior was tested in real conditions, using a research vessel at sea, where the UAV was able to detect, locate, and safely land on top of the developed landing platform.
Practical object detection systems are highly desired to be open-ended for learning on frequently evolved datasets. Moreover, learning with little supervision further adds flexibility for real-world applications such ...
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Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more th...
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Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consump...
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This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency ...
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ISBN:
(数字)9798331528614
ISBN:
(纸本)9798331528621
This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely.
Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an ac...
Domain knowledge exists in various forms, including text, ontologies, graphs, images, audio, and videos. In plant disease detection, most works solely utilize images with disease labels, neglecting textual description...
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ISBN:
(数字)9798350349399
ISBN:
(纸本)9798350349405
Domain knowledge exists in various forms, including text, ontologies, graphs, images, audio, and videos. In plant disease detection, most works solely utilize images with disease labels, neglecting textual descriptions of visual disease symptoms used by human experts for diagnosis. These text descriptions and sample images aid expert identification of visual symptoms. We propose a novel method that leverages text descriptions and image data by modeling domain-specific knowledge about visual symptoms in leaf images as separate feature channels. Each channel corresponds to specific features whose absence or presence in the image influences model predictions. We introduce a channel attention-guided fusion module for weighting each channel based on the input and corresponding output. The combined feature channels are transformed into a standardized 3-channel input format, which can then be processed by any pre-trained convolutional neural network (CNN) as input for feature extraction and subsequent classification. Furthermore, intermediate activations of the channel attention layer combined with the weights from the fusion layer make model predictions explainable. Experimental results on three publicly available datasets of apple and cucumber leaf diseases demonstrate improvements of up to 5% utilizing various state-of-the-art CNN architectures, indicating the efficacy of incorporating textual disease descriptions using the proposed approach.
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