Performance improvement in VSLAM using stabilized feature points

Simultaneous localization and mapping (SLAM) is the main prerequisite for the autonomy of a mobile robot. In this paper, we present a novel method that enhances the consistency of the map using stabilized corner features. The proposed method integrates template matching based video stabilization and Harris corner detector. Extracting Harris corner features from stabilized video consistently increases the accuracy of the localization. Data coming from a video camera and odometry are fused in an Extended Kalman Filter (EKF) to determine the pose of the robot and build the map of the environment. Simulation results validate the performance improvement obtained by the proposed technique

Behavioral Mapless Navigation Using Rings

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot

Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping

Recent work on simultaneous trajectory estimation and mapping (STEAM) for mobile robots has found success by representing the trajectory as a Gaussian process. Gaussian processes can represent a continuous-time trajectory, elegantly handle asynchronous and sparse measurements, and allow the robot to query the trajectory to recover its estimated position at any time of interest. A major drawback of this approach is that STEAM is formulated as a batch estimation problem. In this paper we provide the critical extensions necessary to transform the existing batch algorithm into an extremely efficient incremental algorithm. In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization. Finally, we demonstrate the approach and its advantages on both synthetic and real datasets.Comment: 10 pages, 10 figure

Robot localization and path planning based on potential field for map building in static environments

In static environments, and regarding the landmarks also as obstacles in the given situation, this paper suggests a map building algorithm of simultaneous localization and path planning based on the potential field. The robot can locate its movement control discipline with the help of a potential field theory and by conducting simultaneous localization and mapping; besides, the following prediction and state estimation will be done based on predicted control law. With the method of path planning in the potential field, the minimum influential range of  space obstacles with repulsive potential can be adjusted, which is in adaptation to the landmarks and environments in which the landmarks are simultaneously regarded as obstacles. The experiments show that the suggested algorithm, through which the robot  can conduct simultaneous localization and mapping in the localized landmarks, is also at the same time used as an obstacle in environments. After analyzing relevant performance indicators, the suggested algorithm has been verified as consistent estimation

A Basic Study on Path Teaching Method for a Mobile Robot Using a Digital Camera

This paper proposes a novel path teaching method for a mobile robot. In this method an operator takes pictures at significant points such as turning points, half way points and a destination on robot´s path by a digital camera. Then, the operator teaches the robot landmarks to recognize the images and actions for the robot to take at the significant points. The robot travels autonomously searching the landmarks and obeys the instructions when it recognizes reaching the significant points. By using this method, it is possible to teach paths for the mobile robot more easily. In this paper, outline of the proposed method and results of fundamental experiments in both indoor and outdoor environment to confirm possibility of the method are described

Near minimum time path planning for bearing-only localisation and mapping

The main contribution of this paper is an algorithm for integrating motion planning and simultaneous localisation and mapping (SLAM). Accuracy of the maps and the robot locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the robot. Appropriate control of the robot motion is particularly important in bearing-only SLAM, where the information from a moving sensor is essential. In this paper a near minimum time path planning algorithm with a finite planning horizon is proposed for bearing-only SLAM. The objective of the algorithm is to achieve a predefined mapping precision while maintaining acceptable vehicle location uncertainty in the minimum time. Simulation results have shown the effectiveness of the proposed method. © 2005 IEEE