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Lifelong, Learning-Augmented Robot Navigation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Lifelong, Learning-Augmented Robot Navigation./
Author:	Doherty, Kevin J.
Description:	1 online resource (157 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Datasets. -
Online resource:	click for full text (PQDT)
ISBN:	9798380097376

Lifelong, Learning-Augmented Robot Navigation.
Doherty, Kevin J.

Lifelong, Learning-Augmented Robot Navigation. - 1 online resource (157 pages)

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Massachusetts Institute of Technology, 2023.

Includes bibliographical references

Simultaneous localization and mapping (SLAM) is the process by which a robot constructs a global model of an environment from local observations of it; this is a fundamental perceptual capability supporting planning, navigation, and control. We are interested in improving the expressiveness and operational longevity of SLAM systems. In particular, we are interested in leveraging state-of-the-art machine learning methods for object detection to augment the maps robots can build with object-level semantic information. To do so, a robot must combine continuous geometric information about its trajectory and object locations with discrete semantic information about object classes. This problem is complicated by the fact that object detection techniques are often unreliable in novel environments, introducing outliers and making it difficult to determine the correspondence between detected objects and mapped landmarks. For robust long-term navigation, a robot must contend with these discrete sources of ambiguity. Finally, even when measurements are not corrupted by outliers, long-term SLAM remains a challenging computational problem: typical solution methods rely on local optimization techniques that require a good "initial guess," and whose computational expense grows as measurements accumulate.The first contribution of this thesis addresses the problem of inference for hybrid probabilistic models, i.e. models containing both discrete and continuous states we would like to estimate. These problems frequently arise when modeling e.g., outlier contamination (where binary variables indicate whether a measurement is corrupted), or when performing object-level mapping (where discrete variables may represent measurement-landmark correspondence or object categories). The former application is crucial for designing more robust perception systems. The latter application is especially important for enabling robots to construct semantic maps; that is, maps containing objects whose states are a mixture of continuous geometric information and discrete semantic information. The second contribution of this thesis is a novel spectral initialization method which is efficient to compute, easy to implement, and admits the first formal performance guarantees for a SLAM initialization method. The final contribution of this thesis aims to curtail the growing computational expense of long-term SLAM. In particular, we propose an efficient algorithm for graph sparsification capable of reducing the computational burden of SLAM methods without significantly degrading SLAM solution quality. Taken together, these contributions improve the robustness and efficiency of robot perception approaches in the lifelong setting.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380097376Subjects--Topical Terms:

1372549
Datasets.
Index Terms--Genre/Form:

554714
Electronic books.

Lifelong, Learning-Augmented Robot Navigation.
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502 $a Thesis (Ph.D.)--Massachusetts Institute of Technology, 2023.
504 $a Includes bibliographical references
520 $a Simultaneous localization and mapping (SLAM) is the process by which a robot constructs a global model of an environment from local observations of it; this is a fundamental perceptual capability supporting planning, navigation, and control. We are interested in improving the expressiveness and operational longevity of SLAM systems. In particular, we are interested in leveraging state-of-the-art machine learning methods for object detection to augment the maps robots can build with object-level semantic information. To do so, a robot must combine continuous geometric information about its trajectory and object locations with discrete semantic information about object classes. This problem is complicated by the fact that object detection techniques are often unreliable in novel environments, introducing outliers and making it difficult to determine the correspondence between detected objects and mapped landmarks. For robust long-term navigation, a robot must contend with these discrete sources of ambiguity. Finally, even when measurements are not corrupted by outliers, long-term SLAM remains a challenging computational problem: typical solution methods rely on local optimization techniques that require a good "initial guess," and whose computational expense grows as measurements accumulate.The first contribution of this thesis addresses the problem of inference for hybrid probabilistic models, i.e. models containing both discrete and continuous states we would like to estimate. These problems frequently arise when modeling e.g., outlier contamination (where binary variables indicate whether a measurement is corrupted), or when performing object-level mapping (where discrete variables may represent measurement-landmark correspondence or object categories). The former application is crucial for designing more robust perception systems. The latter application is especially important for enabling robots to construct semantic maps; that is, maps containing objects whose states are a mixture of continuous geometric information and discrete semantic information. The second contribution of this thesis is a novel spectral initialization method which is efficient to compute, easy to implement, and admits the first formal performance guarantees for a SLAM initialization method. The final contribution of this thesis aims to curtail the growing computational expense of long-term SLAM. In particular, we propose an efficient algorithm for graph sparsification capable of reducing the computational burden of SLAM methods without significantly degrading SLAM solution quality. Taken together, these contributions improve the robustness and efficiency of robot perception approaches in the lifelong setting.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
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650 4 $a Localization. $3 1091696
650 4 $a Robotics. $3 561941
650 4 $a Computer science. $3 573171
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710 2 $a Massachusetts Institute of Technology. $b Department of Aeronautics and Astronautics. $3 1464825
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