Performance comparison of point and plane features for SLAM /

Download
2014
Yörük, Mücahit
Simultaneous Localization and Mapping (SLAM) is an indispensable capability for mobile robots that explore unknown environments. This advanced method is now widely employed since the development of improvements in sensor technology, such as 3D depth cameras. To avoid the risk of the human interaction in dangerous environments, various SLAM algorithms have been developed and proposed in the literature. The aim of this study, is to develop a landmark vector that improves the SLAM performance using the planar features of objects. In order to achieve this goal we generated a fastSLAM algorithm and two different feature extraction methods. The first feature extraction method is SURF, which gives responses at the edges of the depth images and the second feature extraction method is plane detection, which gives a compact representation of the environment. Throughout this thesis, four different landmark vectors are defined (SURF point, plane as point, plane as oriented point and plane as surface) and compared the effects on the SLAM. The advantages of using planar features are shown with both the RGBD SLAM dataset and the real time application.

Suggestions

Development of a visual object localization module for mobile robots
Şahin, Erol (1999-01-01)
Reports preliminary results from the design and implementation of a visual object localization module for mobile robots. The module takes an object-based approach to visual processing and relies on a preprocessing step that segments objects from the image. By tracking the size and the eccentricity of the objects in the image while the robot is moving, the visual object localization module can determine the position of objects relative to the robot using the displacement obtained from its odometry. In locali...
Improving the accuracy of a mobile robot for localization and mapping of an unknown environment
Gümrükcü, Gülşah; Konukseven, Erhan İlhan; Department of Mechanical Engineering (2003)
This thesis deals with sensor based motion planning of a mobile robot for localization in an unknown environment. Using the developed algorithm the robot may construct the map (GVG) of any bounded environment, and the minimum distance between any two locations in the mapped environment can be determined. In addition, the accuracy of the robot, facing dead-reckoning error can be improved. With this study, the mobile robot finds the optimum path between any two locations in any bounded environment and traces ...
Performance Evaluation of Different Real-Time Motion Controller Topologies Implemented on a FPGA
MUTLU, B. R.; Yaman, Ulaş; Dölen, Melik; Koku, Ahmet Buğra (2009-11-18)
This paper presents a comprehensive comparison of several real-time motion controller topologies implemented on a field programmable gate array (FPGA). Controller topologies are selected as proportional-integral-derivative controller with command feedforward, sliding mode controller, fuzzy controller, and a hysteresis controller. Controllers and other necessary modules are developed using Verilog HDL and they are implemented on a ML505 development board with a Xilinx Virtex-5 FPGA chip. In order to take ful...
Performance Evaluation of the Grid-based FastSLAM in V-RFP Using MATLAB
Azak, Salim (2018-02-24)
This paper presents a Simultaneous Localization and Mapping (SLAM) application that is developed in V-REP robot simulation program by using Grid-Based FastSLAM method. In this work, the SLAM problem in an unknown indoor environment is solved with the Pioneer 3 DX mobile robot equipped with a laser range finder. Control scripts are developed in the Lua and FastSLAM scripts using MATLAB that is linked to the simulation platform by means of the Remote API feature of the V-REP. In order to evaluate of the perfo...
An Implementation of 3D slam with planar segments
Turunç, Çağrı; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2012)
Localization and mapping are vital capabilities for a mobile robot. These two capabilities strongly depend on each other and simultaneously executing both of these operations is called SLAM (Simultaneous Localization and Mapping). SLAM problem requires the environment to be represented with an abstract mapping model. It is possible to construct a map from point cloud of environment via scanner sensor systems. On the other hand, extracting higher level of features from point clouds and using these extracted ...
Citation Formats
M. Yörük, “Performance comparison of point and plane features for SLAM /,” M.S. - Master of Science, Middle East Technical University, 2014.