Claus S. Andersen

Optimal landmark selection for triangulation of robot position

Abstract A mobile robot can identify its own position relative to a global environment model by using triangulation based on three landmarks in the environment. It is shown that this procedure may be very sensitive to noise depending on spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position. In addition an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously base its position computation on the set of available landmarks, which provides the least noise sensitive position estimate. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in uncertainty.

Landmark-based navigation strategies

A mobile robot can identify its own position relative to a global environment model using triangulation based on measuring angular separation between three landmarks in the environment. Multiple views from different locations of a smaller set of landmarks can also be used though. Alternatively the current position estimates can be updated using heading and distance measurements to a single landmark. Using these different strategies 8 position estimating techniques have been designed, analyzed and compared. These are based on viewing 1, 2 and 3 landmarks from one or two different viewpoints. It is shown that these procedures may be very sensitive to noise depending on the spatial landmark configuration, and relative position between robot and landmarks. A general analysis is presented which permits prediction of the uncertainty in the triangulated position. The uncertainty measure can be used to determine which of the light different techniques is the most suitable in specific situations. The entire analysis is based on a basic statistical approach, and verified experimentally. In addition to the evaluation of the individual techniques, an algorithm is presented for automatic selection of optimal landmarks. This algorithm enables a robot to continuously estimate its current position from the set of landmarks which provides the most stable solution. It is demonstrated that using this algorithm can result in more than one order of magnitude reduction in position uncertainty.

Navigation using range images on a mobile robot

Abstract We describe an integrated navigation system for an autonomous mobile robot using a laser range camera to obtain knowledge about the environment. The implemented system maintains a 2D world model (floor map) by integrating knowledge obtained from several range images acquired as the robot moves around in its attempt to find a path to the goal position. A path planner uses the floor map to generate collision-free paths consisting of sequences of configurations. Car-like kinematic constraints ensure smooth paths that can be send directly to a wheel controller. The system was implemented on the HERMIES-III robot, a vehicle with 3 degrees of freedom, and tested in both laboratory and simulated environments. These tests showed that a simple integration of the environment modeler and the path planner provides the robot with basic explorative and navigational capabilities. In particular, the system is capable of performing total re-planning in cases where the initial path to the goal point turns out to be blocked.

Vinav a system for VIsion supported NAVigation

We describe a highly modular vision svstem enabling a mobile robot to navigate in a lab environment. The system uses a coarse model of the la, motion information from the mobile robot, and input from a single video camera to achieve this goal. The system consists of the following modules: Filtering of the input. Image to remove noise. Edge extraction. Line linking. Prediction of lines from t he model. Matching of extracted and predicted lines. Estimation of robot position based on rnatched lines. The system has been implemented in order to have a testbed facilitating experiments with either improvements to existing modules, or completely new modules. In its present state the vision system is able to determine an accurate estimate of the position of the robot thus correcting the inevitable errors of the motion sensors of the mobile robot.

Control of perception in dynamic vision

To achieve continuous operation and thus facilitate use of vision in a dynamic scenario, it is necessary to introduce a purpose for the visual processing. This provides information that may control the visual processing and thus limits the amount of resources needed to obtain the required results. A proposed architecture for vision systems is presented, along with an architecture for visual modules. This architecture enables both goal and data driven processing, with a potentially changing balance between the two modes. To illustrate the potential of the proposed architecture, a sample system for recovery of scene depth is presented, with experimental results which demonstrate a scalable performance.