Michael Montemerlo

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The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the(More)
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot’s software system relied predominately on state-of-the-art arti cial intelligence technologies, such as machine learning and probabilistic reasoning. This article describes the major(More)
Proceedings of IJCAI 2003 In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM(More)
Simultaneous Localization and Mapping (SLAM) is an essential capability for mobile robots exploring unknown environments. The Extended Kalman Filter (EKF) has served as the de-facto approach to SLAM for the last fifteen years. However, EKF-based SLAM algorithms suffer from two well-known shortcomings that complicate their application to large, real-world(More)
In this paper we describe our open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit. The ultimate goals of CARMEN are to lower the barrier to implementing new algorithms on real and simulated robots and to facilitate sharing of research and algorithms between different institutions. In order for CARMEN to be as inclusive of(More)
This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable(More)
This paper describes a mobile robotic assistant, developed to assist elderly individuals with mild cognitive and physical impairments, as well as support nurses in their daily activities. We present three software modules relevant to ensure successful human–robot interaction: an automated reminder system; a people tracking and detection system; and finally(More)
This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article(More)
This article presents the architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously. In doing so, the vehicle is able to select its own routes, perceive and interact with other traffic, and execute various urban driving skills including lane changes, U-turns, parking, and merging into moving traffic. The vehicle(More)
The Extended Kalman Filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, realword environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative(More)