Hardware and software considerations for monocular SLAM in a riverine environment
Miller, Martin Hudson
Loading…
Permalink
https://hdl.handle.net/2142/99412
Description
Title
Hardware and software considerations for monocular SLAM in a riverine environment
Author(s)
Miller, Martin Hudson
Issue Date
2017-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Hutchinson, Seth A.
Chung, Soon-Jo
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-03-13T15:49:12Z
Keyword(s)
Simultaneous localization and mapping (SLAM)
Monocular
Stereo
River
Inertial measurement unit (IMU)
Camera
Robotics
Inertial
Abstract
A monocular SLAM algorithm for use on rivers is proposed and compared to existing methods using a newly created SLAM dataset. The proposed algorithm uses a single camera and inertial measurements to estimate the location of a canoe and a map of a river simultaneously using an extended Kalman filter. The algorithm exploits the reflections of map landmarks in the river in order to obtain a depth estimate from a single view. Landmark reflections are found by using the state covariance matrix of the extended Kalman filter to define a search region where reflections are likely to be found. A process noise model is proposed to more accurately reflect the noise characteristics of the inertial measurement unit. The dataset used for the experiments was collected from a canoe on the Sangamon River covering 2.7 kilometers in 44 minutes and divided into eight subsets. Data collected includes stereo images, inertial measurements, and GPS position data for ground truth. The proposed algorithm is evaluated by measuring the translation and attitude error with respect to ground truth and comparisons are made to the stereo method, ORB-SLAM2.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
