rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Vision-Based Stair Detection and Terrain Classification Algorithms for Multi-Terrain Mobile Robots
  name:
    name part: Kalyanram, Vishnu Prashanth
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Lee, Kiju
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: This thesis presents vision-based stair detection and environment classification algorithms for mobile robots capable of traversing staircases and different types of terrains. These algorithms are developed for a specific hardware platform, called Robot{}, which is equipped with wheel-and-leg transformable mechanism enabling multi-terrain locomotion. The design of the hardware platform is optimized to allow for climbing over irregular terrains and continuous obstacles, such as staircases. It is equipped with Jetson TX2 as the main processing board, an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) for odometry, and a Light Detection And Ranging (LiDAR) device and an RGB-Depth (RGB-D) camera. The stair detection algorithm takes the color and depth image feed from the RGB-D camera and uses it to identify straight line patterns that could constitute a stairway. To further embed the robot with the terrain classification capability, the color images are segmented into traversable and non-traversable regions, thereby making urban environments more accessible. Taking the computational limitations into account, it is explored how these schemes can be integrated into the robot navigation stack using Robot Operating System (ROS). The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/197306
  note: by Vishnu Prashanth Kalyanram.
  note: "Major Subject: Mechanical Engineering"
  note: Includes vita.
  note: Master of Science Texas A&M University 2022 https://hdl.handle.net/1969.1/197306
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Mechanical Engineering
  subject:
    topic: Ground robots
  subject:
    topic: Vision Algorithms
  subject:
    topic: Staircase
  subject:
    topic: Terrain classification
  subject:
    topic: SVM
  subject:
    topic: Semantic Segmentation
  subject:
    topic: ROS
  subject:
    topic: Navigation
  identifier: https://hdl.handle.net/1969.1/197306
  record info:
    record content source: TXA
    record creation date: 240819
    record change date: 20240820145856.3
    record identifier: in00005463558
    language of cataloging:
      language term: eng