[show abstract][hide abstract] ABSTRACT: 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.
Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on; 09/2005
[show abstract][hide abstract] ABSTRACT: In this paper, we present an approach of calculating visual odometry for outdoor robots equipped with a stereo rig. Instead of the typical feature matching or tracking, we use an improved stereo-tracking method that simultaneously decides the feature displacement in both cameras. Based on the matched features, a three-point algorithm for the resulting quadrifocal setting is carried out in a RANSAC framework to recover the unknown odometry. In addition, the change in rotation can be derived from infinity homography, and the remaining translational unknowns can be obtained even faster consequently . Both approaches are quite robust and deal well with challenging conditions such as wheel slippage
Image Processing, 2006 IEEE International Conference on; 11/2006
[show abstract][hide abstract] ABSTRACT: This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments
suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage
of EKF-SLAM with scan correlation. Instead of geometric models, landmarks are defined by templates composed of raw sensed
data, and scan correlation is shown to produce landmark observations compatible with the standard EKF-SLAM framework. The
resulting Scan-SLAM combines the general applicability of scan correlation with the established advantages of an EKF implementation: recursive
data fusion that produces a convergent map of landmarks and maintains an estimate of uncertainties and correlations. Experimental
results are presented which validate the algorithm.
Field and Service Robotics, Results of the 5th International Conference, FSR 2005, July 29-31, 2005, Port Douglas, QLD, Australia; 01/2005
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