This paper presents a visual odometry method that estimates the location and orientation of a robotic rover platform. The visual odometry approach is based on Fourier-Mellin transforms and phase-only matched niters wh...
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The illumination conditions of a scene create intra-class variability in outdoor visual data, degrading the performance of high-level algorithms. Using only the image, and with hyper-spectral data as a case study, thi...
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ISBN:
(纸本)9781467399623
The illumination conditions of a scene create intra-class variability in outdoor visual data, degrading the performance of high-level algorithms. Using only the image, and with hyper-spectral data as a case study, this paper proposes a deep learning approach to learn illumination invariant features from the data in an unsupervised manner. The proposed approach incorporates a similarity measure, the Spectral Angle, that is relatively insensitive to brightness into the cost function of a Stacked Auto-Encoder so that an illumination invariant mapping is learned from the input data to the hidden layer. Experiments using synthetic and real imagery show that this novel feature learning approach produces a more illumination invariant representation of the data, improving the results of a high-level algorithm (clustering) under such conditions.
This paper presents a novel framework for integrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object de...
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This paper presents a novel framework for integrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object detection and their motion estimation is developed. Scan-matching and moving object detection are two important problems for full autonomy of robotic systems in complex dynamic environments. Popular techniques for solving these problems usually address each task in turn disregarding important dependencies. The model developed here jointly reasons about these tasks by performing inference in a probabilistic graphical model. It allows different but related problems to be expressed in a single framework. The experiments demonstrate that jointly reasoning results in better estimates for both tasks compared to solving the tasks individually.
We present a transportable system for ocean observations in which a small autonomous surface vehicle (ASV) adaptively collects spatially diverse samples with aid from a team of inexpensive, passive floating sensors kn...
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autonomous navigation has revolutionized agriculture by enabling robots to interact with the environment autonomously. This article presents an integrated system that addresses two key aspects: generalizing environmen...
autonomous navigation has revolutionized agriculture by enabling robots to interact with the environment autonomously. This article presents an integrated system that addresses two key aspects: generalizing environments using LiDAR perception and neural networks, and achieving optimal solutions for nonlinear systems with low computational cost. To achieve precise and efficient navigation in complex agronomic environments, the system combines LiDAR sensors and deep learning methods, specifically utilizing a ResNet-based neural network architecture. LiDAR sensors provide accurate and detailed information on terrain, crops, and obstacles, while the ResNet architecture enhances perception capabilities by extracting and analyzing features from LiDAR point cloud data. For smooth and accurate trajectory following, the system employs the iLQR algorithm, which calculates control commands using an optimization-based control method for nonlinear systems. This algorithm ensures robust guidance of the robot along the desired trajectory. By integrating LiDAR perception with the ResNet-based deep learning approach and iLQR control, the system enhances the navigation capabilities of agricultural robots, resulting in reduced operational costs, increased efficiency, and minimized environmental impact.
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