Improving Robustness and Precision in Mobile Robot Localization by Using Laser Range Finding and Monocular Vision

This paper discusses mobile robot localization by means of geometric features from a laser range finder and a CCD camera. The features are line segments from the laser scanner and vertical edges from the camera. Emphasis is put on sensor models with a strong physical basis. For both sensors, uncertainties in the calibration and measurement process are adequately modeled and propagated through the feature extractors. This yields observations with their first order covariance estimates which are passed to an extended Kalman filter for fusion and position estimation. Experiments on a real platform show that opposed to the use of the laser range finder only, the multisensor setup allows the uncertainty to stay bounded in difficult localization situations like long corridors and contributes to an important reduction of uncertainty, particularly in the orientation. The experiments further demonstrate the applicability of such a multisensor localization system in real-time on a fully autonomous robot

Sensordatenfusion zur Robusten und präzisen EKF Lokalisierung von mobilen Robotern

Diese Arbeit beschreibt einen Ansatz zur Lokalisierung von Mobilrobotern mittels der Kombination eines Laserscanners mit monokularem Video. Das Verfahren ist merkmalsbasiert und benutzt ein erweitertes Kalman filter (EKF) zur Datenfusion und Positionsschätzung. Die Umgebungsmerkmale sind Liniensegmente für den Laserscanner und vertikale Kanten für die Kamera. Physikalisch gut basierte Unsicherheitsmodelle beider Sensoren werden eingesetzt und bei Sensorkalibration und Merkmalsextraktion in Betracht gezogen. Dies liefert die geschätzten ersten zwei Momente der Merkmalsvektoren. Die Experimente, die auf einem vollständig autonomen Roboter durchgeführt wurden, zielten auf zwei Fragestellungen ab: In welchem Mass kann das Hinzufügen video-basierter Umgebungsinformation die Navigation hinsichtlich Robustheit und Präzision verbessern? Die dazu ausgeführten Experimente zeigen, dass gerade in schwierigen Lokalisierungsszenarien wie lange Korridore, die Bildinformation einen unerlässlichen Beitrag liefert und in der Lage ist, die Positionsschätzung im allgemeinen und besonders in der Orientierung zu verbessern

Open Challenges in SLAM: An Optimal Solution Based on Shift and Rotation Invariants

This paper starts with a discussion of the open challenges in the SLAM problem. In our opinion they can be grouped in two main and distinct areas: convergence of the built map and computation requirement for real world application. To deal with the previous problems, a solution in the stochastic map framework based on the concept of the relative map is proposed. The idea consists in introducing a map state, which only contains quantities invariant under shift and rotation and to carry out the estimation of this relative map in an optimal way. This is a possible way in order to have a decoupling between the robot motion and the landmark estimation and therefore not to rely the landmark estimation on the unmodeled error sources of the robot motion. Moreover, the proposed solution scales linearly with the number of landmark allowing real-time application. Experimental results, carried out on a real platform, show the better performance of this method with respect to the joint vehicle-landmark approach (absolute map fflter) when the odometry is affected by undetected systematic errors or by large or unmodeled non-systematic errors

The Need for Autonomy and Real-Time in Mobile Robotics: A Case Study of XO/2 and Pygmalion

Starting from a user point of view the paper discusses the requirements of a development environment (operating system and programming language) for mechatronic systems, especially mobile robots. We argue that user requirements from research, education, ergonomics and applications impose a certain functionality on the embedded operating system and programming language, and that a deadline-driven real-time operating system helps to fulfil these requirements. A case study of the operating system XO/2, its programming language Oberon-2 and the mobile robot Pygmalion is presented. XO/2 explicitly addresses issues like scalabilty, safety and abstraction, previously found to be relevant for many user scenarios

Simultaneous Localization and Map Building: A Global Topological Model with Local Metric Maps

In this paper an approach combining the metric and topological paradigm for simultaneous localization and map building is presented. The main idea is to connect local metric maps by means of a global topological map. This allows a compact environment model which does not require global metric consistency and permits both precision and robustness. The method uses a 360 degree laser scanner in order to extract corners and openings for the topological approach and lines for the metric localization. The approach has been tested in a 30 x 25 m portion of the institute building with the fully autonomous robot Donald Duck. An experiment consists of a complete exploration and a set of test missions. Three experiments have been performed for a total of 15 test missions, which have been randomly defined and completed with a success ratio of 87%

Topological Global Localization and Mapping with Fingerprint and Uncertainty

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported

Combining Topological and Metric: A Natural Integration for Simultaneous Localization and Map Building

In this paper the metric and topological paradigm are integrated in a single system for both localization and map building. A global topological map connects local metric maps, allowing a compact environment model, which does not require global metric consistency and permits both precision and robustness. Furthermore, the approach permits to handle loops in the environment by automatic mapping using the information of the multimodal topological localization. The system uses a 360 degree laser scanner to extract corners and openings for the topological approach and lines for the metric method. This hybrid approach has been tested in a 50 x 25 m2 portion of the institute building with the fully autonomous robot Donald Duck. Experiments are of three types: Maps created by a complete exploration of the environment are compared to estimate their quality; Test missions are randomly generated in order to evaluate the efficiency of the localization approach; The third type of experiments shows the practicability of the approach for closing the loop