Bridging the gap between feature- and grid-based SLAM

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  • iua1. Introduction

    Buildingmaps is one of the fundamental tasks of mobile robots.In the literature, the mobile robot mapping problem is oftenreferred to as the simultaneous localization and mapping (SLAM)problem. It is considered to be a complex problem, because forlocalization a robot needs a consistent map of the environment,and for acquiring a map, a robot requires a good estimate of itslocation. This mutual dependency between the estimates aboutthe pose of the robot and the map of the environment makes theSLAMproblem hard and involves searching for a solution in a high-dimensional space.A large variety of different estimation techniques has been

    proposed to address the SLAM problem. Extended Kalman filters,sparse extended information filters, maximum likelihood meth-ods, particle filters, and several other techniques have been appliedto estimate the trajectory of the robot as well as a map of the en-vironment. Most approaches to mapping use a single scheme forrepresenting the environment. Among the most popular ones arefeature-based models such as sets of landmarks or dense repre-sentations such as occupancy grids. In a practical robotic applica-tion, the decision of which model to use is largely influenced bythe type of the environment the robot is deployed in. In large openspaces with predefined landmarks, for example, feature-based ap-proaches often are preferred, whereas occupancy grid maps havewidely been used in unstructured environments. In real world

    Corresponding author.E-mail (K.M. Wurm).

    scenarios, however, one generally cannot assume that the envi-ronment is uniformly covered by specific features. Consider, forexample, a surveillance system which can operate both inside ofbuildings and outside on parking spaces or large outdoor storageareas. Such a system has to be capable of dynamically choosing thebest representation in each area to maximize its robustness.The contribution of this paper is a novel approach which

    allows a mobile robot to utilize different representations of theenvironment. In the example of a combination of feature-basedmodels with occupancy grid maps, we describe how a robot canperform the mapping process using both types of representation.It applies reinforcement learning to select the representation thatis best suited tomodel the area surrounding the robot based on thecurrent sensor observations and the state of the filter. We applythe approach in the context of a RaoBlackwellized particle filtertomaintain the joint posterior about the trajectory of the robot andthe map of the environment.As we will demonstrate in the experiments, our approach out-

    performs pure grid and pure feature-based approaches. Further-more, our approach allows for the modeling of heterogeneousenvironments which cannot be adequately represented by eitherof the single representations. A motivating example is shown inFig. 1. Here, the environment consists of outdoor and indoor parts.A feature-based representation is well suited tomodel the outdoorpart (Fig. 1(a)) but cannot be used to correct odometry errors insidethe buildings due to the lack of relevant features. A grid-based rep-resentation, in contrast, leads to false matches in the outdoor partsdue to the sparsity of non max-range measurements there but ac-curately represents the inside of the buildings (see Fig. 1(b)). Oursystem combines the advantages of both representations to gener-ate a consistent map (Fig. 1(c)).Robotics and Autonomous S

    Contents lists availa

    Robotics and Auto

    journal homepage: www.

    Bridging the gap between feature- and grKai M. Wurm , Cyrill Stachniss, Giorgio GrisettiUniversity of Freiburg, Department of Computer Science, Georges-Khler-Allee 79, 79110 F

    a r t i c l e i n f o

    Article history:Available online 22 September 2009

    Keywords:SLAMFeaturesGrid mapsLearningDual representation

    a b s t r a c t

    One important design decischoice of the representationshould be used, or whether athis paper, we present an apronment simultaneously. It umethod depending on the cmate based on the representhas been implemented on aand outdoors and therefore spractical experiments, we dewhich cannot be adequately0921-8890/$ see front matter 2009 Elsevier B.V. All rights reserved.doi:10.1016/j.robot.2009.09.009ystems 58 (2010) 140148

    ble at ScienceDirect

    nomous Systems

    id-based SLAM

    reiburg, Germany

    on for the development of autonomously navigating mobile robots is theof the environment. This includes the question of which type of featuresdense representation such as occupancy grid maps is more appropriate. Inproach which performs SLAM using multiple representations of the envi-ses reinforcement to learn when to switch to an alternative representationrrent observation. This allows the robot to update its pose and map esti-tion that models the surrounding of the robot in the best way. The approachreal robot and evaluated in scenarios, in which a robot has to navigate in-witches between a landmark-based representation and a dense grid map. Inmonstrate that our approach allows a robot to robustly map environmentsmodeled by either of the individual representations.

    2009 Elsevier B.V. All rights reserved.

  • n(c) Combining features and grid maps.

    Fig. 1. When mapping environments that contain large open spaces with few landmarks as well as dense structures, a combination of feature maps and grids mapsoutperforms the individual techniques.

    This paper is organized as follows. After a discussion of relatedwork, we briefly introduce the SLAM approach utilized in thispaper, namely RaoBlackwellized particle filters, in Section 3.Whereas Section 4 presents our approach for mapping with a dualrepresentation of the environment, Section 5 explains our modelselection technique based on reinforcement learning. Finally, wepresent experimental results obtained in simulation and on realrobots in Section 6.

    2. Related work

    Mapping techniques for mobile robots can be roughly classifiedaccording to the map representation and the underlying estima-tion technique. One popular map representation is the occupancygrid [1]. Whereas such grid-based approaches are computationallyexpensive and typically require a huge amount of memory, theyare able to represent arbitrary objects. It should be noted that tocorrect the robot pose estimate a certain amount of obstacles inthe range of the robots sensor is needed. This can be a problem ifthe range of the sensor is short as is the case with small scale laserscanners or if the environment is a large open area.Feature-based representations are attractive because of their

    compactness. This is a clear advantage in terms of memoryconsumption and processing speed. However, such systems rely onpredefined feature extractors, which assume that some structuresin the environments are known in advance. This clearly limits thefield of action of a robot.The model of the environment and the applied state estimation

    landmarks. The effectiveness of the EKF approaches results fromthe fact that they estimate a fully correlated posterior aboutlandmark maps and robot poses [2,3]. Their weakness lies in thestrong assumptions that have to bemade on both the robotmotionmodel and the sensor noise. Moreover, the landmarks are assumedto be uniquely identifiable. There exist techniques [4] to deal withunknown data association in the SLAM context, however, if theseassumptions are violated, the filter is likely to diverge [57].Thrun et al. [8] proposed a method that uses the inverse of the

    covariance matrix. The advantage of the sparse extended infor-mation filters (SEIFs) is that they make use of the approximativesparsity of the informationmatrix and in thisway can performpre-dictions and updates in constant time. Eustice et al. [9] presenteda technique to make use of exactly sparse information matrices ina delayed-state framework.In a work by Murphy, Doucet, and colleagues [10,11], Rao

    Blackwellized particle filters (RBPF) have been introduced as aneffective means to solve the SLAM problem. Each particle in aRBPF represents a possible trajectory of the robot and a map ofthe environment. The framework has been subsequently extendedby Montemerlo et al. [12,13] for approaching the SLAM problemwith landmarkmaps. To learn accurate gridmaps, RBPFs have beenused by Eliazar and Parr [14] and Hhnel et al. [15]. Whereas thefirst work describes an efficient map representation, the secondpresents an improved motion model that reduces the number ofrequired particles. The work of Grisetti et al. [16] describes animproved variant of the algorithm proposed by Hhnel et al. [15]combined with the ideas of FastSLAM2 [12]. Instead of using aK.M. Wurm et al. / Robotics and Auto

    (a) Feature-based mapping system (no features inside the b

    (b) Grid-based mapping system (few structural informationtechnique are often coupled. One of the most popular approachesare extended Kalman filters (EKFs) in combinationwith predefinedomous Systems 58 (2010) 140148 141

    uildings). Features are illustrated by circles.

    outside).fixed proposal distribution, the algorithm computes an improvedGaussian proposal distribution on a per-particle basis on the

  • n142 K.M. Wurm et al. / Robotics and Auto

    fly. A further extension of this method which overcomes thelimitation of the Gaussian assumption has recently been presentedby Stachniss et al. [17]. Additional improvements concerning bothruntime andmemory requirements have been achieved by Grisettiet al. [18] by reusing already computed proposal distributions.So far, there exist only very few methods that try to combine

    feature-based models with grid maps. One is the hybrid metricmap (HYMM) approach [19]. It estimates the location of featuresand pe