Proceedings ArticleDOI
Real-time monocular dense mapping on aerial robots using visual-inertial fusion
Zhenfei Yang,Fei Gao,Shaojie Shen +2 more
- pp 4552-4559
TLDR
A tightly-coupled visual-inertial localization module is designed to provide metric and high-accuracy odometry and a motion stereo algorithm is proposed to take the video input from one camera to produce local depth measurements with semi-global regularization.Abstract:
In this work, we present a solution to real-time monocular dense mapping. A tightly-coupled visual-inertial localization module is designed to provide metric and high-accuracy odometry. A motion stereo algorithm is proposed to take the video input from one camera to produce local depth measurements with semi-global regularization. The local measurements are then integrated into a global map for noise filtering and map refinement. The global map obtained is able to support navigation and obstacle avoidance for aerial robots through our indoor and outdoor experimental verification. Our system runs at 10Hz on an Nvidia Jetson TX1 by properly distributing computation to CPU and GPU. Through onboard experiments, we demonstrate its ability to close the perception-action loop for autonomous aerial robots. We release our implementation as open-source software1.read more
Citations
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Journal ArticleDOI
Autonomous aerial navigation using monocular visual‐inertial fusion
TL;DR: It is shown that it is possible to achieve reliable online autonomous navigation using monocular VINS, and the backbone of the system is a highly accurate optimization‐based monocular visual‐inertial state estimator with online initialization and self‐extrinsic calibration.
Proceedings ArticleDOI
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
TL;DR: NeuralRecon as mentioned in this paper uses a learning-based TSDF fusion module based on gated recurrent units to guide the network to fuse features from previous fragments, which can capture local smoothness prior and global shape prior of 3D surfaces.
Proceedings ArticleDOI
Robust Dense Mapping for Large-Scale Dynamic Environments
TL;DR: A stereo-based dense mapping algorithm for large-scale dynamic urban environments that simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments.
Journal ArticleDOI
Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments
TL;DR: This paper develops a quadrotor platform equipped with a three‐dimensional light detection and ranging (LiDAR) and an inertial measurement unit (IMU) for simultaneously estimating states of the vehicle and building point cloud maps of the environment.
Proceedings ArticleDOI
MVDepthNet: Real-Time Multiview Depth Estimation Neural Network
Kaixuan Wang,Shaojie Shen +1 more
TL;DR: MVDepthNet is presented, a convolutional network to solve the depth estimation problem given several image-pose pairs from a localized monocular camera in neighbor viewpoints, and it is shown that this method can generate depth maps efficiently and precisely.
References
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Proceedings ArticleDOI
KinectFusion: Real-time dense surface mapping and tracking
Richard Newcombe,Shahram Izadi,Otmar Hilliges,David Molyneaux,David Kim,Andrew J. Davison,Pushmeet Kohi,Jamie Shotton,Steve Hodges,Andrew Fitzgibbon +9 more
TL;DR: A system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware, which fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real- time.
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Book ChapterDOI
LSD-SLAM: Large-Scale Direct Monocular SLAM
TL;DR: A novel direct tracking method which operates on \(\mathfrak{sim}(3)\), thereby explicitly detecting scale-drift, and an elegant probabilistic solution to include the effect of noisy depth values into tracking are introduced.
Proceedings ArticleDOI
DTAM: Dense tracking and mapping in real-time
TL;DR: It is demonstrated that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application is shown.
Proceedings ArticleDOI
A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation
TL;DR: The primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints that arise when a static feature is observed from multiple camera poses, and is optimal, up to linearization errors.
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