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- 2Localization
- 2Simultaneous Localization and Mapping
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- 1Artificial intelligence
- 1Automated Vehicle
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Fall 2019
Autonomous navigation has been a popular research topic over the last two decades. The ability for a robot to solve the simultaneous localization and mapping (SLAM) problem is required to navigate unknown environments. One such example is a self-driving car: it needs to build maps of new...
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Spring 2016
With the rapid development in visual sensors such as monocular vision, appearance-based robot simultaneous localization and mapping (SLAM) has become an open research topic in robotics. In appearance SLAM, a robot uses the visual appearance of locations (i.e., the images) acquired along its route...
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Computer Vision-Based Motion Control and State Estimation for Unmanned Aerial Vehicles (UAVs)
DownloadSpring 2018
To achieve a fully autonomous unmanned aerial vehicle (UAV) the vehicle needs a high level of self awareness. At a minimum it needs to know where it is and where it wants to go. Computer vision (CV) is a logical solution to this problem. However, using CV to solve motion control problems for UAVs...
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Fall 2019
This thesis proposes a method to estimate robot localization error without having a ground-truth measurement of robot position. Robot localization refers to estimating a robot position and orientation (pose) within a known map, where the error is the difference between the robot’s ground-truth...
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Spring 2015
Sampling from a given probability distribution is a key problem in many different disciplines. Markov chain Monte Carlo (MCMC) algorithms approach this problem by constructing a random walk governed by a specially constructed transition probability distribution. As the random walk progresses, the...
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Spring 2022
Automated Vehicle (AV) is a trending technology being developed with the promise to reduce traffic accidents caused by human errors. Perception plays a crucial role for Automated Driving Systems (ADS) to make safe decisions. However, local sensory data is insufficient to capture comprehensive...
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Fall 2018
Semi-dense SLAM systems have become popular in the last few years. They can produce much denser point clouds than sparse SLAM while being computationally efficient (using only CPU). In previous works, the surface of the viewed scene was reconstructed in real-time by combining sparse SLAM system...
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Fall 2019
There has been tremendous research progress in estimating the depth of a scene from a monocular camera image. Existing methods for single-image depth prediction are exclusively based on deep neural networks, and their training can be unsupervised using stereo image pairs, supervised using LiDAR...
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Spring 2021
The technique of Simultaneous Localization and Mapping (SLAM) has been widely studied and used in autonomous vehicles. The SLAM algorithms can construct the map from an unknown environment and at the same time, estimate the robot position. These are fundamentals of the autonomous robots, for...