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Autonomous Exploration of Complex Environments Using Active Slam.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Autonomous Exploration of Complex Environments Using Active Slam./
作者:	Mahajan, Aditya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	156 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Unmanned aerial vehicles. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003836
ISBN:	9798209786450

Autonomous Exploration of Complex Environments Using Active Slam.
Mahajan, Aditya.

Autonomous Exploration of Complex Environments Using Active Slam. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 156 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

This dissertation develops a novel algorithm to perform autonomous surveys of unexplored 2D and 3D environments by extending the coverage path planning (CPP) literature to perform surveys using probabilistic coverage estimation.Robotic survey missions are designed to gather data from an environment and produce maps after the robot has been retrieved. The maps can take the form of 2D mosaics for planar terrain and 3D reconstructions for complex 3D environments. Survey missions of this form have applications in search-and-rescue, structural inspection, and scientific study. The goal of the survey mission is to achieve complete coverage, which is to have the robot observe every part of the environment before it is retrieved.Complete coverage becomes an issue when the robot does not have accurate localization, which can happen in environments that experience GPS-denial, e.g. underwater. In such environments, simultaneous localization and mapping (SLAM) is commonly used for localization. SLAM is an approach that corrects odometry with additional information in the form of terrain features. However, SLAM performance can degrade when there are insufficient features available, i.e. the terrain is feature-poor.There has been an evolution of algorithms that addresses the problem of surveying GPS-denied feature-poor terrain. State-of-the-art survey methods plan paths based on a coverage map, an estimate of which parts of the target area have been seen by the robot. This approach is called CPP.This thesis extends the CPP literature by developing an algorithm called SLAM directed by uncertainty-based node connections (SLAM-DUNC). For 2D surveys, SLAM-DUNC incorporates localization uncertainty into the coverage map to calculate aprobability of coverage and plans paths based on this quantity. This extension enables probabilistic coverage estimation with cameras. For 3D surveys, paths are planned using a similar quantity called the probability of occupancy.SLAM-DUNC was demonstrated in simulation and on flight hardware, both in 2D and 3D environments. In 2D, SLAM-DUNC produced less false positive coverage compared to a non-probabilistic coverage method. For surveys in 3D environments, SLAM-DUNC produced an occupancy estimate that maintained accuracy and conservativeness in feature-poor terrain.An additional contribution is the improvement of a known localization algorithm called GraphSLAM to be more robust to false feature correspondences. These improvements are demonstrated using field data.

ISBN: 9798209786450Subjects--Topical Terms:

3560267
Unmanned aerial vehicles.

Autonomous Exploration of Complex Environments Using Active Slam.
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