Nicola Tomatis

Robox at Expo.02: A large-scale installation of personal robots

In this paper we present Robox, a mobile robot designed for operation in a mass exhibition and the experience we made with its installation at the Swiss National Exhibition Expo.02. Robox is a fully autonomous mobile platform with unique multi-modal interaction capabilities, a novel approach to global localization using multiple Gaussian hypotheses, and a powerful obstacle avoidance. Eleven Robox ran for 12 hours daily from May 15 to October 20, 2002, traveling more than 3315 km and interacting with 686,000 visitors.

A comparison of line extraction algorithms using 2D range data for indoor mobile robotics

Abstract This paper presents an experimental evaluation of different line extraction algorithms applied to 2D laser scans for indoor environments. Six popular algorithms in mobile robotics and computer vision are selected and tested. Real scan data collected from two office environments by using different platforms are used in the experiments in order to evaluate the algorithms. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with ground truth using standard statistical methods. An extended case study is performed to further evaluate the algorithms in a SLAM application.

Hybrid Simultaneous Localization and Map Building: a Natural Integration of Topological and Metric

In this paper the metric and topological paradigms are integrated in a hybrid 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 handles loops in the environment during automatic mapping by means of the information of the multimodal topological localization. The system uses a 360 ◦ 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 m × 25 m portion of the institute building with the fully autonomous robot Donald Duck. Experiments are of four 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 approach for both the localization and relocation; the fourth type of experiments shows the practicability of the approach for closing the loop.

Multisensor on-the-fly localization: Precision and reliability for applications

This paper presents an approach for localization using geometric features from a 360 laser range finder and a monocular vision system. Its practicability under conditions of continuous localization during motion in real time (referred to as on-the-fly localization) is investigated in large-scale experiments. The features are infinite horizontal lines for the laser and vertical lines for the camera. They are extracted using physically well-grounded models for all sensors and passed to a Kalman filter for fusion and position estimation. Positioning accuracy close to subcentimeter has been achieved with an environment model requiring 30 bytes/m 2 . Already with a moderate number of matched features, the vision information was found to further increase this precision, particularly in the orientation. The results were obtained with a fully self-contained system where extensive tests with an overall length of more than 6.4 km and 150,000 localization cycles have been conducted. The final testbed for this localization system was the Computer 2000 event, an annual computer tradeshow in Lausanne, Switzerland, where during 4 days visitors could give high-level navigation commands to the robot via a web interface. This gave us the opportunity to obtain results on long-term reliability and verify the practicability of the approach under application-like conditions. Furthermore, general aspects and limitations of multisensor on-the-fly localization are discussed.

Real-time obstacle avoidance for polygonal robots with a reduced dynamic window

In this paper we present an approach to obstacle avoidance and local path planning for polygonal robots. It decomposes the task into a model stage and a planning stage. The model stage accounts for robot shape and dynamics using a reduced dynamic window. The planning stage produces collision-free local paths with a velocity profile. We present an analytical solution to the distance to collision problem for polygonal robots, avoiding thus the use of look-up tables. The approach has been tested in simulation and on two non-holonomic rectangular robots where a cycle time of 10 Hz was reached under full CPU load. During a long-term experiment over 5 km travel distance, the method demonstrated its practicability.

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential drive.

Simultaneous localization and odometry calibration for mobile robot

This paper presents both the theory and the first experimental results of a new method which allows simultaneous estimation of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic components is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.

Orthogonal SLAM: a Step toward Lightweight Indoor Autonomous Navigation

Today, lightweight SLAM algorithms are needed in many embedded robotic systems. In this paper the orthogonal SLAM (OrthoSLAM ) algorithm is presented and empirically validated. The algorithm has constant time complexity in the state estimation and is capable to run real-time. The main contribution resides in the idea of reducing the complexity by means of an assumption on the environment. This is done by mapping only lines that are parallel or perpendicular to each other which represent the main structure of most indoor environments. The combination of this assumption with a Kalman filter and a relative map approach is able to map our laboratory hallway with the size of 80 m times 50 m and a trajectory of more than 500 m. The precision of the resulting map is similar to the measurements done by hand which are used as the ground-truth

Improving robustness and precision in mobile robot localization by using laser range finding and monocular vision

The 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.

A hybrid approach for robust and precise mobile robot navigation with compact environment modeling

In this paper a new localization approach combining the metric and topological paradigm 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 lines for the metric localization and doors, discontinuities and hallways for the topological approach. The approach has been widely tested in a 50/spl times/25 m portion of the institute building with the new fully autonomous robot Donald Duck. 25 randomly generated test missions were performed with a success ratio of 96% and a mean error at the goal point of 9 mm for an overall trajectory length of 1.15 km.