Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input.
Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations.
The best variations of Texture MCL improved accuracy by 19.1\\% and 25.1\\% when all particles and the top fifty particles respectively were used to calculate the robot's estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot.
","abstract_html":"<p>Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input.</p> <p>Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations.</p> <p>The best variations of Texture MCL improved accuracy by 19.1\\% and 25.1\\% when all particles and the top fifty particles respectively were used to calculate the robot's estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot.</p>","abstract_has_math":false,"creators":["Krapil, Stephanie"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["John Seng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-09-01T07:00:00Z","date_published":"2014-09-01T07:00:00Z","updated_at":"2026-07-24T01:31:35Z","subjects":["Robot Localization","Monte Carlo Localization","MCL","Neural Network","IMU","Laser Range Finder","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.157"],"render_values":[{"text":"10.15368/theses.2014.157","href":"https://doi.org/10.15368/theses.2014.157","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1326","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John Seng"]},{"key":"dc:creator","label":"Author","values":["Krapil, Stephanie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-25T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robot Localization","Monte Carlo Localization","MCL","Neural Network","IMU","Laser Range Finder","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1326","10.15368/theses.2014.157"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input.
Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations.
The best variations of Texture MCL improved accuracy by 19.1\\% and 25.1\\% when all particles and the top fifty particles respectively were used to calculate the robot's estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot.
"]},{"key":"dc:title","label":"Title","values":["Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data"]}]}],"canonical_facts":{"dc:contributor":["John Seng"],"dc:creator":["Krapil, Stephanie"],"dc:date.available":["2014-11-25T08:00:00Z"],"dc:description.abstract":["Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input.
Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations.
The best variations of Texture MCL improved accuracy by 19.1\\% and 25.1\\% when all particles and the top fifty particles respectively were used to calculate the robot's estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1326","10.15368/theses.2014.157"],"dc:subject":["Robot Localization","Monte Carlo Localization","MCL","Neural Network","IMU","Laser Range Finder","Artificial Intelligence and Robotics"],"dc:title":["Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:35Z"}