- Email: [email protected]
Mobile robot localization and map building: a multisensor fusion approach
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Book reviews / Automatica 39 (2003) 1113 – 1124
Mobile robot localization and map building: a multisensor fusion approach JosFe A. Castellanos and Juan D. TardFos; Kluwer Academic Publishers, Dordrecht, ISBN: 0-7923-7789-3 Localisation is a fundamental competence for autonomous mobile robot navigation. Knowledge of the vehicle pose-position and orientation—is requisite for many higher level tasks such as path planning and control decisions. In a previously unexplored environment, where an a priori map is not available,1 localisation may be accomplished by building an incremental model of the environment via exteroceptive sensors, and concurrently using this map to determine the vehicle pose. This problem is termed simultaneous localisation and mapping (SLAM) and has been the focus of considerable research over the past decade or so, particularly following the seminal work of Smith, Self, and Cheeseman (1987) on stochastic maps. This book is a revision of the doctoral dissertation of Castellanos (1998). It presents several new contributions to the 7eld of localisation and mapping in the context of indoor wheeled mobile robots. In particular, it addresses four aspects of the localisation problem: map feature (or landmark) representation, feature extraction from raw sensor data, observation-to-map data association, and the development of a more stable and general formulation of the stochastic SLAM algorithm. The following review discusses the book’s treatment of these four areas. A feature-based stochastic map, as proposed by Smith et al. (1987), is presently the only map framework suitable for consistent SLAM. This style of map consists of geometric primitives that represent observable landmarks in the environment. The map is termed stochastic as the feature parameters are expressed by their nominal values (i.e., mean) and their uncertainties (i.e., covariance). Chapter 2 of the book introduces a new representation for this type of uncertain geometric information called the symmetries and perturbations model (SP-model). Rather than de7ning a feature by a mean and covariance, it is represented by a fourtuple: the nominal location of a coordinate frame embedded at the feature’s centre, a perturbation vector representing the error in the nominal location, the uncertainty of the perturbation estimate, and a binding matrix to account for symmetries in the feature’s structure. (Note, symmetries are a property of a geometric primitive. For example, an in7nite line feature is symmetric along its length, and a 2-D point feature is symmetric to rotation about its centre.) The SP-model, and its application to SLAM in Chapter 6, is arguably the primary contribution of this book. It is claimed to provide a uniform representation for any geometric primitive, and to avoid over-parametrisation and singularity problems. That is, it provides a representation that is uniform, minimal and stable. 1 For example, an a priori map commonly utilised in outdoor environments is GPS, which consists of active beacons (satellites) at precisely known locations above the earth’s surface.
Chapters 3 and 4 are concerned with feature extraction from laser and vision data. In Chapter 3, line segments are extracted from scanning range laser data via a two-step process. First, the scan is partitioned at discontinuities in consecutive range measurements, which are found using a Kalman 7lter tracker with a constant acceleration model for the predicted change in range values. Second, within each continuous partition, lines are 7tted using an adaptation of a recursive line-7tting algorithm from Ballard and Brown (1982). In Chapter 4, the laser line segments are described by two higher level features: corners, de7ned by the intersection of two consecutive lines, and semiplanes, de7ned by line segment free-endpoints (i.e., segment endpoints at range discontinuities). To provide multi-sensor redundancy with these laser features, vertical lines are extracted from monocular vision data using the Burns algorithm (Burns, Hanson, & Riseman, 1986). The alignment of a vision and laser feature serves to con7rm the validity of the feature and also to improve the accuracy of the feature estimate. Chapter 5 is concerned with batch data association between map features and features extracted from sensor data, particularly for the case where the location of the sensor in the map is initially unknown. Associating sensed features as a batch has the advantage that associations are constrained by their relative locations and joint compatibility, which is much stronger than associating features individually. Two algorithms are presented: identifying before locating (IBW) and identifying while locating (IWL). Both facilitate association without prior sensor pose information, and provide the same degree of constraint, but IWL is shown to be computationally more e9cient. However, recent research has produced several new batch association algorithms that are superior to IWL: joint compatibility branch and bound (JCBB) (Neira & TardFos, 2001), combined constraint data association (CCDA) (Bailey, 2002), and random sampling joint compatibility (RSJC) (Neira, TardFos, & Castellanos, 2002). (Note, JCBB assumes that the sensor pose is at least partially known.) These new algorithms are far more e9cient, and permit stronger association constraints, than IBL or IWL. The 7nal contribution of this book is the SP-map SLAM algorithm in Chapter 6. This formulation of stochastic SLAM uses the SP-model to provide a uniform feature representation for di6erent features and sensors. Thus, SP-map SLAM inherits the advantages of the SP-model mentioned above. One particularly interesting aspect of SP-map SLAM is the “centreing” step, performed after each observation update, where the state vector is relinearised to make the perturbation vector equal to zero. This step is claimed to prevent bias in the estimate, which has been reported as an issue for the traditional (i.e., Smith et al.) SLAM algorithm. This is an interesting claim, but it requires theoretical and experimental proof via comparison with the traditional SLAM algorithm (e.g., similar to the comparison provided in the relocation-fusion paper of Moutarlier and Chatila, 1989).
Book reviews / Automatica 39 (2003) 1113 – 1124
Overall, this is a di9cult book to read. The grammar and phrasing is often awkward and the main contributions tend to be obscured by confusing explanations. Also, the notation and equation style are unusual and some e6ort is required, on behalf of the reader, to become accustomed to their appropriate interpretation. The book is the revision of a doctoral thesis, and so it is not an introductory text or a survey. Rather, it presents a particular novel approach to several aspects of the SLAM problem. As such, the audience for this book will be researchers in the 7eld, familiar with the mobile robot navigation literature, and specially with stochastic SLAM. Finally, mobile robot navigation is a fast-moving area of research, and some contributions in the book are already dated. As mentioned above, the batch data association algorithms have recently been superseded. On the other hand, the SP-model and SP-map (presented also in Castellanos, Montiel, Neira, & TardFos, 1999) comprise the most promising aspects of the book, and provide an interesting avenue for further research. Particularly, it is important to demonstrate the advantages of the SP-map in terms of numerical stability, ease of implementation (e.g., bookkeeping for disparate feature types), and susceptibility to bias. Tim Bailey E-mail address: [email protected] Hugh Durrant-Whyte Australian Centre for Field Robotics; The University of Sydney; The Rose Street Building J04 Sydney NSW 2006; Australia
1121
References Bailey, T. (2002). Mobile robot localisation and mapping in extensive outdoor environments. Ph.D. Thesis, University of Sydney, Australian Centre for Field Robotics. Ballard, D. H., & Brown, C. M. (1982). Computer vision. Englewood Cli6s, NJ: Prentice-Hall. Burns, J. B., Hanson, A. R., & Riseman, E. M. (1986). Extracting straight lines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(4), 425–455. Castellanos, J. A. (1998). Mobile robot localization and map building: A multisensor fusion approach. Ph.D. Thesis, Department of Computer Science and Systems Engineering, University of Zaragoza, 1998. Castellanos, J. A., Montiel, J. M. M., Neira, J., & TardFos, J. D. (1999). The SPmap: A probabilistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 15(5), 948–952. Moutarlier, P., & Chatila, R. (1989). Stochastic multisensory data fusion for mobile robot location and environmental modelling. In Fifth international symposium of robotics research (pp. 85 –94). Neira, J., & TardFos, J. D. (2001). Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation, 17(6), 890–897. Neira, J., TardFos, J. D., & Castellanos, J. A. (2002). Linear time vehicle relocation in SLAM. Technical Report, Department of Computer Science and Systems Engineering, University of Zaragoza. Smith, R., Self, M., & Cheeseman, P. (1987). A stochastic map for uncertain spatial relationships. In Fourth international symposium of robotics research (pp. 467– 474). About the reviewers Tim Bailey is a postdoctoral researcher, and Hugh Durrant-Whyte is a Professor at the Australian Centre for Field Robotics (ACFR) at The University of Sydney, Australia. They both have an interest in SLAM.
doi:10.1016/S0005-1098(03)00066-9
Analytic feedback system design: an interpolation approach Peter Dorato; Brooks and Cole, Paci7c Grove, CA, 2000, ISBN: 0-534-36917-0 1. Introduction Control has been referred to as the “hidden technology” because of its essential importance to many devices and systems while being mainly out of sight. The evolution of cheap and powerful digital computers as well as computer-aided design tools have had a major impact on control system design and controller implementation. At the moment, the 7eld is going through fundamental transformations. Signi7cant interactions with communications, computer science, and biotechnology are adding to the already existing applications in aerospace and process control. One thing is for sure: our 7eld is much more interdisciplinary than ever before. I can personally attest to this 7rst hand working closely with aerospace, mechanical, electrical, and materials engi-
neers as well as physicists and chemists. The developments in this decade are set to re-invigorate the 7eld as a dynamic multi-disciplinary one. Over the last three decades, numerous introductory control textbooks have been written. The control theory after World War II was nicely summarized in the book by Truxal (1955), and many introductory control textbooks are now available (Franklin, Powell, & Emami-Naeini, 2002; BFelanger, 1995; D’Azzo & Houpis, 1995; Dorf & Bishop, 2001; Kuo, 1995; Nise, 2000; Ogata, 2002; Phillips & Harbor, 2000), but the essential classical control theory content do not di6er much from that of Truxal’s. One notable trend has been to include more state-space material and promote the use of digital control (Franklin, Powell, & Workman, 1998), and computer-aided design tools where MATLABJ is now the standard tool. There has been a healthy discussion among our colleagues in academia and industry on the suitable contents of an in troductory course in control. The traditional curriculum has been criticized as not keeping up with the times. The criticisms range from questioning the need to even teach con-
Book reviews / Automatica 39 (2003) 1113 – 1124
Mobile robot localization and map building: a multisensor fusion approach JosFe A. Castellanos and Juan D. TardFos; Kluwer Academic Publishers, Dordrecht, ISBN: 0-7923-7789-3 Localisation is a fundamental competence for autonomous mobile robot navigation. Knowledge of the vehicle pose-position and orientation—is requisite for many higher level tasks such as path planning and control decisions. In a previously unexplored environment, where an a priori map is not available,1 localisation may be accomplished by building an incremental model of the environment via exteroceptive sensors, and concurrently using this map to determine the vehicle pose. This problem is termed simultaneous localisation and mapping (SLAM) and has been the focus of considerable research over the past decade or so, particularly following the seminal work of Smith, Self, and Cheeseman (1987) on stochastic maps. This book is a revision of the doctoral dissertation of Castellanos (1998). It presents several new contributions to the 7eld of localisation and mapping in the context of indoor wheeled mobile robots. In particular, it addresses four aspects of the localisation problem: map feature (or landmark) representation, feature extraction from raw sensor data, observation-to-map data association, and the development of a more stable and general formulation of the stochastic SLAM algorithm. The following review discusses the book’s treatment of these four areas. A feature-based stochastic map, as proposed by Smith et al. (1987), is presently the only map framework suitable for consistent SLAM. This style of map consists of geometric primitives that represent observable landmarks in the environment. The map is termed stochastic as the feature parameters are expressed by their nominal values (i.e., mean) and their uncertainties (i.e., covariance). Chapter 2 of the book introduces a new representation for this type of uncertain geometric information called the symmetries and perturbations model (SP-model). Rather than de7ning a feature by a mean and covariance, it is represented by a fourtuple: the nominal location of a coordinate frame embedded at the feature’s centre, a perturbation vector representing the error in the nominal location, the uncertainty of the perturbation estimate, and a binding matrix to account for symmetries in the feature’s structure. (Note, symmetries are a property of a geometric primitive. For example, an in7nite line feature is symmetric along its length, and a 2-D point feature is symmetric to rotation about its centre.) The SP-model, and its application to SLAM in Chapter 6, is arguably the primary contribution of this book. It is claimed to provide a uniform representation for any geometric primitive, and to avoid over-parametrisation and singularity problems. That is, it provides a representation that is uniform, minimal and stable. 1 For example, an a priori map commonly utilised in outdoor environments is GPS, which consists of active beacons (satellites) at precisely known locations above the earth’s surface.
Chapters 3 and 4 are concerned with feature extraction from laser and vision data. In Chapter 3, line segments are extracted from scanning range laser data via a two-step process. First, the scan is partitioned at discontinuities in consecutive range measurements, which are found using a Kalman 7lter tracker with a constant acceleration model for the predicted change in range values. Second, within each continuous partition, lines are 7tted using an adaptation of a recursive line-7tting algorithm from Ballard and Brown (1982). In Chapter 4, the laser line segments are described by two higher level features: corners, de7ned by the intersection of two consecutive lines, and semiplanes, de7ned by line segment free-endpoints (i.e., segment endpoints at range discontinuities). To provide multi-sensor redundancy with these laser features, vertical lines are extracted from monocular vision data using the Burns algorithm (Burns, Hanson, & Riseman, 1986). The alignment of a vision and laser feature serves to con7rm the validity of the feature and also to improve the accuracy of the feature estimate. Chapter 5 is concerned with batch data association between map features and features extracted from sensor data, particularly for the case where the location of the sensor in the map is initially unknown. Associating sensed features as a batch has the advantage that associations are constrained by their relative locations and joint compatibility, which is much stronger than associating features individually. Two algorithms are presented: identifying before locating (IBW) and identifying while locating (IWL). Both facilitate association without prior sensor pose information, and provide the same degree of constraint, but IWL is shown to be computationally more e9cient. However, recent research has produced several new batch association algorithms that are superior to IWL: joint compatibility branch and bound (JCBB) (Neira & TardFos, 2001), combined constraint data association (CCDA) (Bailey, 2002), and random sampling joint compatibility (RSJC) (Neira, TardFos, & Castellanos, 2002). (Note, JCBB assumes that the sensor pose is at least partially known.) These new algorithms are far more e9cient, and permit stronger association constraints, than IBL or IWL. The 7nal contribution of this book is the SP-map SLAM algorithm in Chapter 6. This formulation of stochastic SLAM uses the SP-model to provide a uniform feature representation for di6erent features and sensors. Thus, SP-map SLAM inherits the advantages of the SP-model mentioned above. One particularly interesting aspect of SP-map SLAM is the “centreing” step, performed after each observation update, where the state vector is relinearised to make the perturbation vector equal to zero. This step is claimed to prevent bias in the estimate, which has been reported as an issue for the traditional (i.e., Smith et al.) SLAM algorithm. This is an interesting claim, but it requires theoretical and experimental proof via comparison with the traditional SLAM algorithm (e.g., similar to the comparison provided in the relocation-fusion paper of Moutarlier and Chatila, 1989).
Book reviews / Automatica 39 (2003) 1113 – 1124
Overall, this is a di9cult book to read. The grammar and phrasing is often awkward and the main contributions tend to be obscured by confusing explanations. Also, the notation and equation style are unusual and some e6ort is required, on behalf of the reader, to become accustomed to their appropriate interpretation. The book is the revision of a doctoral thesis, and so it is not an introductory text or a survey. Rather, it presents a particular novel approach to several aspects of the SLAM problem. As such, the audience for this book will be researchers in the 7eld, familiar with the mobile robot navigation literature, and specially with stochastic SLAM. Finally, mobile robot navigation is a fast-moving area of research, and some contributions in the book are already dated. As mentioned above, the batch data association algorithms have recently been superseded. On the other hand, the SP-model and SP-map (presented also in Castellanos, Montiel, Neira, & TardFos, 1999) comprise the most promising aspects of the book, and provide an interesting avenue for further research. Particularly, it is important to demonstrate the advantages of the SP-map in terms of numerical stability, ease of implementation (e.g., bookkeeping for disparate feature types), and susceptibility to bias. Tim Bailey E-mail address: [email protected] Hugh Durrant-Whyte Australian Centre for Field Robotics; The University of Sydney; The Rose Street Building J04 Sydney NSW 2006; Australia
1121
References Bailey, T. (2002). Mobile robot localisation and mapping in extensive outdoor environments. Ph.D. Thesis, University of Sydney, Australian Centre for Field Robotics. Ballard, D. H., & Brown, C. M. (1982). Computer vision. Englewood Cli6s, NJ: Prentice-Hall. Burns, J. B., Hanson, A. R., & Riseman, E. M. (1986). Extracting straight lines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(4), 425–455. Castellanos, J. A. (1998). Mobile robot localization and map building: A multisensor fusion approach. Ph.D. Thesis, Department of Computer Science and Systems Engineering, University of Zaragoza, 1998. Castellanos, J. A., Montiel, J. M. M., Neira, J., & TardFos, J. D. (1999). The SPmap: A probabilistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 15(5), 948–952. Moutarlier, P., & Chatila, R. (1989). Stochastic multisensory data fusion for mobile robot location and environmental modelling. In Fifth international symposium of robotics research (pp. 85 –94). Neira, J., & TardFos, J. D. (2001). Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation, 17(6), 890–897. Neira, J., TardFos, J. D., & Castellanos, J. A. (2002). Linear time vehicle relocation in SLAM. Technical Report, Department of Computer Science and Systems Engineering, University of Zaragoza. Smith, R., Self, M., & Cheeseman, P. (1987). A stochastic map for uncertain spatial relationships. In Fourth international symposium of robotics research (pp. 467– 474). About the reviewers Tim Bailey is a postdoctoral researcher, and Hugh Durrant-Whyte is a Professor at the Australian Centre for Field Robotics (ACFR) at The University of Sydney, Australia. They both have an interest in SLAM.
doi:10.1016/S0005-1098(03)00066-9
Analytic feedback system design: an interpolation approach Peter Dorato; Brooks and Cole, Paci7c Grove, CA, 2000, ISBN: 0-534-36917-0 1. Introduction Control has been referred to as the “hidden technology” because of its essential importance to many devices and systems while being mainly out of sight. The evolution of cheap and powerful digital computers as well as computer-aided design tools have had a major impact on control system design and controller implementation. At the moment, the 7eld is going through fundamental transformations. Signi7cant interactions with communications, computer science, and biotechnology are adding to the already existing applications in aerospace and process control. One thing is for sure: our 7eld is much more interdisciplinary than ever before. I can personally attest to this 7rst hand working closely with aerospace, mechanical, electrical, and materials engi-
neers as well as physicists and chemists. The developments in this decade are set to re-invigorate the 7eld as a dynamic multi-disciplinary one. Over the last three decades, numerous introductory control textbooks have been written. The control theory after World War II was nicely summarized in the book by Truxal (1955), and many introductory control textbooks are now available (Franklin, Powell, & Emami-Naeini, 2002; BFelanger, 1995; D’Azzo & Houpis, 1995; Dorf & Bishop, 2001; Kuo, 1995; Nise, 2000; Ogata, 2002; Phillips & Harbor, 2000), but the essential classical control theory content do not di6er much from that of Truxal’s. One notable trend has been to include more state-space material and promote the use of digital control (Franklin, Powell, & Workman, 1998), and computer-aided design tools where MATLABJ is now the standard tool. There has been a healthy discussion among our colleagues in academia and industry on the suitable contents of an in troductory course in control. The traditional curriculum has been criticized as not keeping up with the times. The criticisms range from questioning the need to even teach con-