SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input
This paper investigates simultaneous localization and mapping (SLAM) problem by exploiting the Microsoft Kinect™ sensor array and an autonomous mobile robot capable of self-localization. The combination of them covers the major features of SLAM including mapping, sensing, locating, and modeling. The...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
ASME Press
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/22225 |
| _version_ | 1848750811065090048 |
|---|---|
| author | Wurdemann, H. Georgiou, E. Cui, Lei Dai, J. |
| author2 | Primo Zingaretti |
| author_facet | Primo Zingaretti Wurdemann, H. Georgiou, E. Cui, Lei Dai, J. |
| author_sort | Wurdemann, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper investigates simultaneous localization and mapping (SLAM) problem by exploiting the Microsoft Kinect™ sensor array and an autonomous mobile robot capable of self-localization. The combination of them covers the major features of SLAM including mapping, sensing, locating, and modeling. The Kinect™ sensor array provides a dual camera output of RGB, using a CMOS camera, and RGB-D, using a depth camera. The sensors will be mounted on the KCLBOT, an autonomous nonholonomic two wheel maneuverable mobile robot. The mobile robot platform has the ability to self-localize and preform navigation maneuvers to traverse to set target points using intelligent processes. The target point for this operation is a fixed coordinate position, which will be the goal for the mobile robot to reach, taking into consideration the obstacles in the environment which will be represented in a 3D spatial model. Extracting the images from the sensor after a calibration routine, a 3D reconstruction of the traversable environment is produced for the mobile robot to navigate. Using the constructed 3D model the autonomous mobile robot follows a polynomial-based nonholonomic trajectory with obstacle avoidance. The experimental results demonstrate the cost effectiveness of this off the shelf sensor array. The results show the effectiveness to produce a 3D reconstruction of an environment and the feasibility of using the Microsoft Kinect™ sensor for mapping, sensing, locating, and modeling, that enables the implementation of SLAM on this type of platform. |
| first_indexed | 2025-11-14T07:42:45Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22225 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:42:45Z |
| publishDate | 2011 |
| publisher | ASME Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-222252017-09-13T13:50:22Z SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input Wurdemann, H. Georgiou, E. Cui, Lei Dai, J. Primo Zingaretti Kinect 3D reconstruction SLAM This paper investigates simultaneous localization and mapping (SLAM) problem by exploiting the Microsoft Kinect™ sensor array and an autonomous mobile robot capable of self-localization. The combination of them covers the major features of SLAM including mapping, sensing, locating, and modeling. The Kinect™ sensor array provides a dual camera output of RGB, using a CMOS camera, and RGB-D, using a depth camera. The sensors will be mounted on the KCLBOT, an autonomous nonholonomic two wheel maneuverable mobile robot. The mobile robot platform has the ability to self-localize and preform navigation maneuvers to traverse to set target points using intelligent processes. The target point for this operation is a fixed coordinate position, which will be the goal for the mobile robot to reach, taking into consideration the obstacles in the environment which will be represented in a 3D spatial model. Extracting the images from the sensor after a calibration routine, a 3D reconstruction of the traversable environment is produced for the mobile robot to navigate. Using the constructed 3D model the autonomous mobile robot follows a polynomial-based nonholonomic trajectory with obstacle avoidance. The experimental results demonstrate the cost effectiveness of this off the shelf sensor array. The results show the effectiveness to produce a 3D reconstruction of an environment and the feasibility of using the Microsoft Kinect™ sensor for mapping, sensing, locating, and modeling, that enables the implementation of SLAM on this type of platform. 2011 Conference Paper http://hdl.handle.net/20.500.11937/22225 10.1115/DETC2011-47735 ASME Press restricted |
| spellingShingle | Kinect 3D reconstruction SLAM Wurdemann, H. Georgiou, E. Cui, Lei Dai, J. SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title | SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title_full | SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title_fullStr | SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title_full_unstemmed | SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title_short | SLAM Using 3D reconstruction via a visual RGB and RGB-D sensory input |
| title_sort | slam using 3d reconstruction via a visual rgb and rgb-d sensory input |
| topic | Kinect 3D reconstruction SLAM |
| url | http://hdl.handle.net/20.500.11937/22225 |