MINERVA: a second-generation museum tour-guide robot

Thrun, Sebastian; Bennewitz, Maren; Burgard, Wolfram; Cremers, Armin B.; Dellaert, Frank; Fox, Dieter; Hähnel, Dirk; Rosenberg, Charles; Roy, Nicholas; Schulte, Jamie; Schulz, Dirk

doi:10.1109/robot.1999.770401

Cited by 522 publications

(307 citation statements)

References 9 publications

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“…Reliable odometric feedback are provided to the control system through the heading from compass and distance data from wheel encoders. A PID algorithm has been implemented to keep the robot (2) along the exact path planned by the vision system in an obstacle free region. The feedback for PID algorithm is the direction of heading obtained from digital compass.…”

Section: Experimental Robotic Platformmentioning

confidence: 99%

Intelligent Indoor Mobile Robot Navigation Using Stereo Vision

Krishnan¹,

Kollipara²

2014

SIPIJ

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Section: Experimental Robotic Platformmentioning

confidence: 99%

Intelligent Indoor Mobile Robot Navigation Using Stereo Vision

Krishnan¹,

Kollipara²

2014

SIPIJ

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“…The wide-scale deployment of autonomous robots has for a long time been hampered by the limitations of the sensors and the algorithms used for robot autonomy. This has led to work on robots like Minerva [1] that used a "coastal planner" to avoid navigation paths with poor information content perceivable to the robot. Exploration and navigation approaches that account for perceptual limitations of robots [2] have been studied as well.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Rhino [4], a robot contender at the 1994 AAAI Robot Competition and Exhibition, used SONAR readings to build an occupancy grid map [5], and localized by matching its observations to expected wall orientations. Minerva [1] served as a tour guide in a Smithsonian museum. It used laser scans and camera images along with odometry to construct two maps, the first being an occupancy grid map, the second a textured map of the ceiling.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-sensor Mobile Robot Localization for Diverse Environments

Biswas

Veloso

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be "perfect," or universally applicable to all situations. Laser rangefinders are commonly used for localization, and state-of-theart algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass walls. In such situations, the error in localization estimates using laser rangefinders could grow in an unbounded manner. Localization algorithms that use depth cameras, like the Microsoft Kinect sensor, have similar characteristics. WiFi signal strength based algorithms, on the other hand, are applicable anywhere there is dense WiFi coverage, and have bounded errors. Although the minimum error of WiFi based localization may be greater than that of laser rangefinder or depth camera based localization, the maximum error of WiFi based localization is bounded and less than that of the other algorithms. Hence, in our work, we analyze the strengths of localization using all three sensors -using a laser rangefinder, a depth camera, and using WiFi. We identify sensors that are most accurate at localization for different locations on the map. The mobile robot could then, for example, rely on WiFi localization more in open areas or areas with glass walls, and laser rangefinder and depth camera based localization in corridor and office environments.

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“…The term named Ceiling Mosaics was first introduced in [7]. The key idea consists of using a digital camera oriented to the ceiling of the environment (focal axis perpendicular to the ceiling plane), and using the ceiling images to compute the map.…”

Section: Introductionmentioning

confidence: 99%

Ceiling mosaics through information-based SLAM

Roda

Sáez

Escolano

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a novel approach to computing ceiling mosaics based on Information Theory. The only sensor of the robot is a digital camera oriented to the ceiling of the map, which is used to approximate the Simultaneous Localization and Mapping (SLAM) problem. We have divided the algorithm into two steps: (i) action estimation, which approximates the actions of the robot maximizing the Mutual Information between consecutive views; and (ii) global rectification, which rectifies the drift of the global trajectory minimizing the entropy of the map. Moreover, a fisheye lens is used to recover enough information from ceilings, reducing their inherent ambiguity. Such lenses produce a semi-spherical aberration in the images, that must be rectified using some information about calibration. In order to do so, we propose a novel technique for image rectification, also based on Information Theory. Finally, we present some experimental results using real data, that prove the robustness of the method.

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MINERVA: a second-generation museum tour-guide robot

Cited by 522 publications

References 9 publications

Intelligent Indoor Mobile Robot Navigation Using Stereo Vision

Intelligent Indoor Mobile Robot Navigation Using Stereo Vision

Multi-sensor Mobile Robot Localization for Diverse Environments

Ceiling mosaics through information-based SLAM

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