Autonomous exploration of complex environments using active SLAM
Abstract/Contents
- Abstract
- 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 a probability 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Mahajan, Aditya |
|---|---|
| Degree supervisor | Rock, Stephen M |
| Thesis advisor | Rock, Stephen M |
| Thesis advisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Schwager, Mac |
| Degree committee member | Kochenderfer, Mykel J, 1980- |
| Degree committee member | Schwager, Mac |
| Associated with | Stanford University, Department of Aeronautics and Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Aditya Mahajan. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/pq664mz1702 |
Access conditions
- Copyright
- © 2021 by Aditya Mahajan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...