In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

The Autonomous City Explorer (ACE) project combines research from autonomous outdoor navigation and human-robot interaction. The ACE robot is capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. It finds its way by interacting with pedestrians in a natural and intuitive way and building a topological representation of its surroundings. In a recent experiment the robot managed to successfully travel a 1.5 km distance from the campus of the Technische Universitat Munchen to Marienplatz, the central square of Munich. This article describes the principles and system components for navigation in urban environments, information retrieval through natural human-robot interaction, the construction of a suitable semantic representation as well as results from the field experiment.

The Autonomous City Explorer: Towards Natural Human-Robot Interaction in Urban Environments

In the past, there has been a tremendous amount of progress in the area of autonomous robot navigation, and a large variety of robots have been developed that demonstrated robust navigation capabilities indoors, in nonurban outdoor environments, or on roads; relatively few approaches have focused on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system, including a simultaneous localization and mapping module for dealing with huge maps of city centers, a planning component for inferring feasible paths, taking into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and the means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than 3-km-long route through the city center of Freiburg, Germany.

Autonomous Robot Navigation in Highly Populated Pedestrian Zones

In this paper, a novel decision-making method is proposed for autonomous mobile robot navigation in an urban area where global positioning system (GPS) measurements are unreliable. The proposed method uses lidar measurements of the road's surface to detect road boundaries. An interacting multiple model method is proposed to determine the existence of a curb based on a probability threshold and to accurately estimate the roadside curb position. The decision outcome is used to determine the source of references suitable for reliable and seamless navigation. The performance of the decision-making algorithm is verified through extensive experiments with a mobile robot autonomously navigating through campus roads with several intersections and unreliable GPS measurements. Our experimental results demonstrate the reliability and good tracking performance of the proposed algorithm for autonomous urban navigation.

A Lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters

Urban search and rescue USAR missions for mobile robots require reliable state estimation systems resilient to conditions given by the dynamically changing environment. We design and evaluate a data fusion system for localization of a mobile skid-steer robot intended for USAR missions. We exploit a rich sensor suite including both proprioceptive inertial measurement unit and tracks odometry and exteroceptive sensors omnidirectional camera and rotating laser rangefinder. To cope with the specificities of each sensing modality such as significantly differing sampling frequencies, we introduce a novel fusion scheme based on an extended Kalman filter for six degree of freedom orientation and position estimation. We demonstrate the performance on field tests of more than 4.4 i¾?km driven under standard USAR conditions. Part of our datasets include ground truth positioning, indoor with a Vicon motion capture system and outdoor with a Leica theodolite tracker. The overall median accuracy of localization-achieved by combining all four modalities-was 1.2% and 1.4% of the total distance traveled for indoor and outdoor environments, respectively. To identify the true limits of the proposed data fusion, we propose and employ a novel experimental evaluation procedure based on failure case scenarios. In this way, we address the common issues such as slippage, reduced camera field of view, and limited laser rangefinder range, together with moving obstacles spoiling the metric map. We believe such a characterization of the failure cases is a first step toward identifying the behavior of state estimation under such conditions. We release all our datasets to the robotics community for possible benchmarking.

/pdf/robust-data-fusion-of-multimodal-sensory-information-for-1l2a1lbzyr.pdf

Robust Data Fusion of Multimodal Sensory Information for Mobile Robots

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors, which include wheel encoders, an inertial measurement unit, a differential global positioning system, and four laser scanner sensors. All the computation was done on a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system's perception, road extraction, and motion planning are detailed. The system was used and tested in a 1-km autonomous robot navigation challenge held in the City of Tsukuba, Japan, named “Tsukuba Challenge 2007.” The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation. © 2009 Wiley Periodicals, Inc.

Autonomous robot navigation in outdoor cluttered pedestrian walkways

Autonomous robot navigation in outdoor cluttered pedestrian walkways: Morales et al.: Autonomous Robot Navigation

This paper presents and describes the approach for achieving long distance autonomous navigation with a mobile robot on outdoor cluttered pedestrian paths. The task was to finish an event launched by the City of Tsukuba in Japan, called ldquoReal World Robot Challengerdquo, of navigating 1km autonomously in a real environment with real pedestrians and bicycles. The hardware, software and strategy for navigating in cluttered environments is explained. Moreover, the complementary functionality of the overall system where map-based and sensor-based navigation seamlessly change, is presented. The robustness of the system is validated with experimental results.

Atsushi Aburadani

Papers

Autonomous robot navigation in outdoor cluttered pedestrian walkways

Autonomous robot navigation in outdoor cluttered pedestrian walkways: Morales et al.: Autonomous Robot Navigation

1Km autonomous robot navigation on outdoor pedestrian paths “running the Tsukuba challenge 2007”