Albert Diosi's Publications

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Publications:

• A. Diosi, "Laser Range Finder and Advanced Sonar Based Simultaneous Localization and Mapping for Mobile Robots", PhD thesis, Monash University, 2005.
  

• A. Diosi and L. Kleeman, "Laser Scan Matching in Polar Coordinates with Application to SLAM " Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05), August, 2005, Edmonton, Canada.
  This paper presents a novel method for 2D laser scan matching called Polar Scan Matching (PSM). The method belongs to the family of point to point matching approaches. Our method avoids searching for point associations by simply matching points with the same bearing. This association rule enables the construction of an algorithm faster than the iterative closest point (ICP). Firstly the PSM approach is tested with simulated laser scans. Then the accuracy of our matching algorithm is evaluated from real laser scans from known relative positions to establish a ground truth. Furthermore, to demonstrate the practical usability of the new PSM approach, experimental results from a Kalman lter implementation of simultaneous localization and mapping (SLAM) are provided.

• A. Diosi and L. Kleeman, "Scan Matching in Polar Coordinates with Application to SLAM " Technical Report MECSE-29-2005, Intelligent Robotics Research Centre, Monash University, 2005.
  This report presents a novel method for 2D laser scan matching called Polar Scan Matching (PSM). The method belongs to the family of point to point matching approaches. Our method avoids searching for point associations by simply matching points with the same bearing. This association rule enables the construction of an algorithm faster than the iterative closest point (ICP). Firstly the PSM approach is tested with simulated laser scans. Then the accuracy of our matching algorithm is evaluated from real laser scans from known relative positions to establish a ground truth. Furthermore, to demonstrate the practical usability of the new PSM approach, experimental results from a Kalman lter implementation of simultaneous localization and mapping (SLAM) are provided.

• A. Diosi, G. Taylor and L. Kleeman, "Interactive SLAM using Laser and Advanced Sonar ", Proceedings of 2005 IEEE ICRA, 2005, Barcelona.
  This paper presents a novel approach to mapping for mobile robots that exploits user interaction to semi-autonomously create a labeled map of the environment. The robot autonomously follows the user and is provided with a verbal commentary on the current location with phrases such as Robot, we are in the office . At the same time, a metric feature map is generated using fusion of laser and advanced sonar measurements in a Kalman filter based SLAM framework, which is later used for localization. When mapping is complete, the robot generates an occupancy grid for use in global task planning. The occupancy grid is created using a novel laser scan registration scheme that relies on storing the path of the robot along with associated local SLAM features during mapping, and later recovering the path by matching the associated local features to the final SLAM map. The occupancy grid is segmented into labelled rooms using an algorithm based on watershed segmentation and integration of the verbal commentary. Experimental results demonstrate our mobile robot creating SLAM and segmented occupancy grid maps of rooms along a 70 metre corridor, and then using these maps to navigate between rooms.

• A. Diosi and L. Kleeman, "Advanced Sonar and Laser Range Finder Fusion for Simultaneous Localization and Mapping " Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'04), September 28 - October 2, 2004, Sendai, Japan.
  Increasing the information content of measurements can ease some of the problems associated with simultaneous localization and mapping (SLAM). We present an approach for combining measurements from a laser range finder with measurements from an advanced sonar array capable of accurate range and bearing measurements and edge, corner and plane classification. In our approach sonar aids laser segmentation, laser aids good sonar point feature selection and laser and sonar measurements of the same object are fused. We also present a novel approach for fitting right angle corners to laser range data, which enables simple error estimation through the minimization of sum of square range residuals. The results are then used for SLAM with a mobile robot.

• A. Diosi and L. Kleeman, "Uncertainty of Line Segments Extracted from Static SICK PLS Laser , Australasian Conference on Robotics and Automation Brisbane Dec 2003.
  Knowing the uncertainty of measurements is important for sensor fusion using Kalman filters. Our sensor of interest is the Sick PLS101-112 laser range finder. We present an approach for straight line parameter estimation in polar coordinates that results in a simple covariance estimate and a worst case systematic line parameter error model based on a range error model backed up by experimental data. We show that our random and systematic line error models match experimental data most of the time. We also demonstrate that systematic line parameter errors can be larger than the random ones.

• A. Diosi and L. Kleeman. "Uncertainty of line segments extracted from static SICK PLS laser scans", Technical Report MECSE-26-2003, Intelligent Robotics Research Centre, Monash University, 2003.
  Data fusion using Kalman filters requires reasonably good error models. Our intention to fuse line segments, corners and edges obtained from a laser scanner and from advanced sonars provided the motivation on the investigation of Sick PLS laser scanner range measurement reliability, and line segment estimation precision. We present an approach for fitting lines straight in the lasers polar coordinate system, which enables a simple estimation of line parameter covariance. We also develop systematic error models for line parameter estimation. Finally we measure our systematic and random error models experimentally, and show, that systematic errors can be larger than random ones.

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Last changed: 14/02/2006