Martin Soika

Grid based fault detection and calibration of sensors on mobile robots

This paper presents a concept for fault detection and calibration of external sensors on mobile robots, based on a grid representation of the environment. For every grid cell consistency measures are derived by evaluating the information stored in them. The measures are then used to deliberate about the condition of the involved sensors. Thereby a mobile robot is equipped with the capability of detecting faulty sensors during its operation. The approach is suitable for unknown environments and is flexible in terms of failure and sensor types. Furthermore, it allows sensor calibration in unknown environments without any reference objects. The approach was verified by several experiments with the autonomous mobile robot ROAMER using ultrasonic sensors.

Large Consistent Geometric Landmark Maps

The autonomous operation of an intelligent service robot in practical applications requires that the robot builds up a map of the environment by itself, even for large environments like supermarkets. This paper presents a solution to the problem of building large consistent maps consisting of geometric landmarks. This solution consists of three basic steps: - incremental extraction of geometric landmarks from range data - recognition of previously mapped parts of the environment and identification of landmarks originating from the same structure and finally - removing the inconsistencies by unifying those landmarks while retaining local relations between the other landmarks. The recognition is based on comparing partial maps of geometric landmarks. This is done by enhancing an individual landmark with features derived from its environment. Care is taken that these features are invariant with respect to missing landmarks, rotation and translation of the map and varying landmark lengths. Based on this set of features, different landmarks originating from the same real world object can be identified. For the purpose of correcting these inconsistencies the geometric relations between landmarks are modeled by links of variable length and variable angles between a link and the adjacent landmarks forming a flexible truss. Replacing two identified landmarks with their mean modifies length and angles of the related links, thus introducing energy into the truss. The overall energy in the truss is minimized by means of numerical optimization resulting in a consistent map. Experience in the field with about 20 robots has shown that it is possible to build up maps of large environments robustly in real-time.

Robustheit autonomer mobiler Systeme gegenüber Sensordefekten

Autonome mobile Systeme agieren in unpraparierten und oft unbekannten Umgebungen. Sie stutzen sich dabei auf Sensorinformation. Sensorfehler gefahrden die Auftragsausfuhrung sowie die Sicherheit sowohl des mobilen Roboters als auch seines Aktionsraums. Die Steuerung autonomer mobiler Systeme musdeshalb gegenuber Sensordefekten robust sein. Diese Sensordefekte mussen sicher erkannt und diagnostiziert werden, um geeignete Masnahmen ergreifen zu konnen.

Gitterkartenbasierte Fehlererkennung und Kalibrierung für Umgebungsensoren autonomer mobiler Systeme (AMS)

Typische Einsatzumgebungen autonomer mobiler Systeme (AMS) [1] zeichnen sich dadurch aus, das sie nur einen niedrigen Grad an Struktur aufweisen und zudem verschiedene dynamische Objekte (Menschen, andere Fahrzeuge) vorhanden sind. Um sich in seiner Umgebung zurechtzufinden, benotigt das AMS ein Modell, das durch Fusion der Sensormessungen erzeugt wird. Dabei mussen die jeweiligen Unzulanglichkeiten des Mesprinzips sowie der Sensorik selbst (systematische Mesfetiler) berucksichtigt werden [2].