814 research outputs found
Large-scale environment mapping and immersive human-robot interaction for agricultural mobile robot teleoperation
Remote operation is a crucial solution to problems encountered in
agricultural machinery operations. However, traditional video streaming control
methods fall short in overcoming the challenges of single perspective views and
the inability to obtain 3D information. In light of these issues, our research
proposes a large-scale digital map reconstruction and immersive human-machine
remote control framework for agricultural scenarios. In our methodology, a DJI
unmanned aerial vehicle(UAV) was utilized for data collection, and a novel
video segmentation approach based on feature points was introduced. To tackle
texture richness variability, an enhanced Structure from Motion (SfM) using
superpixel segmentation was implemented. This method integrates the open
Multiple View Geometry (openMVG) framework along with Local Features from
Transformers (LoFTR). The enhanced SfM results in a point cloud map, which is
further processed through Multi-View Stereo (MVS) to generate a complete map
model. For control, a closed-loop system utilizing TCP for VR control and
positioning of agricultural machinery was introduced. Our system offers a fully
visual-based immersive control method, where upon connection to the local area
network, operators can utilize VR for immersive remote control. The proposed
method enhances both the robustness and convenience of the reconstruction
process, thereby significantly facilitating operators in acquiring more
comprehensive on-site information and engaging in immersive remote control
operations. The code is available at: https://github.com/LiuTao1126/Enhance-SF
Advanced Mapping of the Seafloor Using Sea Vehicle Mounted Sounding Technologies
A large proportion of the Earth’s surface is the deep sea. Numerous fields require access to seafloor topography and geomorphology. With the emergence of different types of underwater vehicles, especially the commercialization of near-seafloor micro-topographical mapping sonars, near-seafloor micro-topographical detection in the deep sea is possible. Near-seafloor micro-topographical exploration allows accurate detection of the seafloor using multibeam echosounder, side-scan sonar, and bathymetric side-scan sonar carried on-board various vehicles, including deep-tow, autonomous underwater vehicles, remotely operated vehicles, and human-occupied vehicles. Near-seafloor micro-topographical detection can obtain more accurate micro-topography and micro-geomorphology of the seafloor compared to full sea depth topographical detection. In this chapter, the basic principles of three types of near-seafloor micro-topographical mapping sonars are analyzed. Then, four types of underwater vehicles that are suitable for near-seafloor micro-topographical mapping are briefly discussed. Factors affecting mapping and detection results are presented using the Jiaolong human-occupied vehicle and its bathymetric side-scan sonar as an example. Next, the entire data processing and mapping methods are described. Finally, two typical detection results obtained by the Jiaolong bathymetric side-scan sonar in deep-sea are given
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
The recent work of Super Characters method using two-dimensional word
embedding achieved state-of-the-art results in text classification tasks,
showcasing the promise of this new approach. This paper borrows the idea of
Super Characters method and two-dimensional embedding, and proposes a method of
generating conversational response for open domain dialogues. The experimental
results on a public dataset shows that the proposed SuperChat method generates
high quality responses. An interactive demo is ready to show at the workshop.Comment: 5 pages, 2 figures, 1 table. Accepted by CVPR2019 Language and Vision
Worksho
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