2017 IEEE International Conference on Robotics and Automation (ICRA) 2017
DOI: 10.1109/icra.2017.7989529
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Real-time monocular dense mapping on aerial robots using visual-inertial fusion

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Cited by 63 publications
(34 citation statements)
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“…Because our proposed method directly operates on point clouds raw data without map maintenance, the proposed motion planning is naturally plug‐in‐and‐use on many other platforms, which produce point clouds. In fact, we have already applied our previous method from F. Gao and Shen () to a vision‐based quadrotor (Yang et al, ) and ground vehicle (C. Shen, Zhang, Li, Gao, & Shen, ) in various types of applications. In the future, we are going to implement our proposed method, which is an advance on our previous method, on other platforms and applications.…”
Section: Discussionmentioning
confidence: 99%
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“…Because our proposed method directly operates on point clouds raw data without map maintenance, the proposed motion planning is naturally plug‐in‐and‐use on many other platforms, which produce point clouds. In fact, we have already applied our previous method from F. Gao and Shen () to a vision‐based quadrotor (Yang et al, ) and ground vehicle (C. Shen, Zhang, Li, Gao, & Shen, ) in various types of applications. In the future, we are going to implement our proposed method, which is an advance on our previous method, on other platforms and applications.…”
Section: Discussionmentioning
confidence: 99%
“…A point cloud, which is an underlying representation of the environment, does not need a discretization of the configuration space or complicated maintenance. It is essentially an unordered set of three-dimensional (3D) raw data, which can be obtained by many sensors, such as by camera in Yang, Gao, and Shen (2017) and by light detection and ranging (LiDAR) in Zhang and Singh (2018). The choice of planning directly on point clouds bypasses the costly map building and maintenance process and achieves the best adaptivity to different sensors in different environments, which is one of the critical requirements for search-and-rescue missions in hazardous scenes.…”
mentioning
confidence: 99%
“…This reconstruction is the backbone for safe and efficient navigation around obstacles. The method used in this work in the continuation of our preliminary results . We use temporal cost aggregation from multiple images from the monocular to eliminate baseline limitation.…”
Section: Monocular Dense Mappingmentioning
confidence: 99%
“…In our previous work, the keyframe was selected by the VINS framework automatically. However, there exist two limitations: …”
Section: Monocular Dense Mappingmentioning
confidence: 99%
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