Vision-Based Mobile Robot Self-localization and Mapping System for Indoor Environment

Localizing accurately and building map of an environment concurrently is a key factor of a mobile robot system. In this system, the robot makes localization and mapping with artificial landmarks and map-based system. It is a process by which a mobile robot can build a map of an environment while continuously determining the location of the robot within the map. The system estimates the robot position in indoor environments using sensors; a camera, three ultrasonic sensors and encoders. The main contribution of this paper is to reduce computational time and improve mapping with map-based system. The self-localization of mobile robot in an indoor environment is advanced through the construction of map based on sensors and recognition of artificial landmarks. Vision based localization system can benefit from using with ultrasonic sensors. From captured images, the system makes landmark detection by using Canny edge detection and Chain-code Approximation algorithms to represent the contour of landmarks by using edge points. The Kalman filter is aimed to accurately estimate position and orientation of the robot using relative distances to walls or artificial landmarks in environments. A robotic system is capable of mapping in an indoor environment and localizing with respect to the map, in real time, using artificial landmarks and sensors

Automatic Grasping Region Extraction Using Shape Profile Based and Geometrical Features Approach

Many applications of robotics include the grasping and manipulation of objects. Working in assembly robotic environments, the robot has to accurately not only locate the part but also to recognize it in readiness for grasping. In order to determine a grasping position, it is necessary to recognize the types of object, and detect portions which are suitable for grasp. According to get the important data clearly and correctly from the images, the detection and extraction methods are essential. This paper is mainly focused on the method of extracting the PCA and Shaped Profile with geometrical feature. Our proposed method is the combination of shapes based approach with the ratio and hole features. The proposed system has been tested successfully to a dataset of 336 images for seven types of common hand tools and achieved good accuracy and less computation complexity for 2D images by using a single camera. The overall recognition accuracy of PCA method with geometrical feature approach is 69.0476% on the same set of test images whereas overall accuracy of shape profile based method with geometrical feature approach is 97.9167%. Base on the experiment, this system is robust for the industrial robots for grasping tasks. This paper intends to implement machine vision system for industrial robotic grasping tasks.

Vision-Based Autonomous Human Tracking Mobile Robot

Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. In order to effectively interact robots with people in close proximity, the systems must first be able to detect, track, and follow people. Following a human with a mobile robot arises in many different service robotic applications. This paper proposes to build an autonomous human tracking mobile robot which can solve the occlusion problem during tracking. The robot can make human tracking efficiently by analysing the information obtained from a camera which is equipped on the top of the robot. The system performs human detection by using Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) algorithms and then uses HSV (Hue Saturation Value) color system for detecting stranger. If the detected human is stranger, robot will make tracking. During the process, the robot needs to track the stranger without missing. So, Kalman filter is used to solve this problem. Kalman filter can estimate the target human when the human is occluded with walls or something. This paper describes the processing results and experimental results of a mobile robot which will track unmarked human efficiently and handle the occlusion using vision sensor and Kalman filter