Synthesis of indoor maps in presence of uncertainty (original) (raw)
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Simultaneous localization and map building for mobile robot navigation
IEEE Robotics & Automation Magazine, 1999
ne of the key objectives in mobile robotics is to gjve an autonomous mobile robot the ability to perform various tasks in an indoor structured environment. In or-<'U der to do that, thr robot has to becomc aware" ofthe environment and its position in it. To achieve that, a localization method that does not intervene with the rnvironment with beacons or marken and is not influenced by unexpected objects has to be integrated with a map-building algorithm that builds and constantly updates the aorld model using sensor data. While in motion, frequent sampling results in a map representation that is approptiatr for modeling inaccurate and noisy range-sensor data, such as those produced by ultrasonic sensors Results on the localization and niaphuildmg problems have been previously presented, but v e~ little effort has been made to deal simultaneously with both problems.
CONCURRENT MAP BUILDING AND LOCALIZATION ON INDOOR DYNAMIC ENVIRONMENTS
International Journal of Pattern Recognition and Artificial Intelligence, 2002
A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments.
Autonomous Devices to Map Rooms and Other Indoor Spaces and Storing Maps in the Cloud
Proccedings of the ICAIIT2016, 2016
The publication presents a three wheeled robot that has been designed to map rooms, halls and other indoor areas. The device uses an ultrasonic sensor for measuring distance, which is later used for both navigation and obstacle detection. Data were used later to compose a matrixthe schematic map of the room. This map could be uploaded to the cloud for later use by other 3rd party devices so they do not have to redo the mapping process again.
Mobile-Robot Map Building from an Advanced Sonar Array and Accurate Odometry
The International Journal of Robotics Research, 1999
This paper describes a mobile robot equipped with a sonar sensor array in a guided feature based map building task in an indoor environment. The landmarks common to indoor environments are planes, corners and edges, and these are located and classified with the sonar sensor array. The map building process makes use of accurate odometry information that is derived from a pair of knife edged unloaded encoder wheels. Discrete sonar observations are incrementally merged into partial planes to produce a realistic representation of environment that is amenable to sonar localisation.
Real-time map building and navigation for autonomous robots in unknown environments
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1998
An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A ⋆ -based planner generates a local path from the current robot position to the goal. Such path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for efficiently building and modifying the environment map, and the iterative application of A ⋆ , a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.
Simultaneous localization and mapping in domestic environments
2001
Navigating autonomously in a domestic environment is a problem that has attracted a great deal of interest in mobile robotics. A robotic system that operates in ordinary furnished rooms without the need of an engineered environment has many different applications such as service, cleaning and surveillance tasks or simply entertainment. Robotic systems that use artificial landmarks or pre-stored maps of the environment are available today. However, these systems are not very flexible. The user must in fact supply a map of the environment, which can be interpreted by the system. This thesis deals with the problem of Simultaneous Localization and Mapping (SLAM). The mobile robot builds a map of an unexplored environment while simultaneously using this map to localize itself. The feature based approach used in this thesis utilizes the Extended Kalman Filter (EKF) machinery to estimate the pose of the robot and the location of the features. This approach is referred to as stochastic mapping. Point features in the environment are robustly extracted from sonar data using triangulation techniques. In addition, this thesis explores a method for recovering from the most common mode of failure of the stochastic mapping approach. This method allows the EKF algorithm to continue in a consistent manner after a failure has been detected. Finally, the thesis presents a method for achieving more accurate navigation by using the architectural properties of most domestic environments. This method drastically improves navigation, when the stochastic mapping algorithm can not be used due to poor quality sensor data. All the algorithms presented in this thesis have been tested and verified in real world experiments.
Real-time indoor mapping for mobile robots with limited sensing
The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010), 2010
Mapping and localization for indoor robotic navigation is a well-studied field. However, existing work largely relies on long range perceptive sensors in addition to the robot's odometry, and little has been done with short range sensors. In this paper, we propose a method for real-time indoor mapping using only short range sensors such as bumpers and/or wall sensors that enable wall-following. The method uses odometry data from the robot's wall-following trajectory, together with readings from bumpers and wall sensors. The method first performs trace segmentation by fitting line segments to the noisy trajectory. Given the assumption of approximately rectilinear structure in the floor plans, typical for most indoor environments, a probabilistic rectification process is then applied to the segmented traces to obtain the orthogonal wall outlines. Both segmentation and rectification are performed on-line onboard the robot during its navigation through the environment. The resulting map is a set of line segments that represents the wall outline. The method has been tested in office buildings. Experimental results have shown that the method is robust to noisy odometry and non-rectilinear obstacles along the walls.
2002
This article provides a comprehensive introduction into the field of robotic mapping, with a focus on indoor mapping. It describes and compares various probabilistic techniques, as they are presently being applied to a vast array of mobile robot mapping problems. The history of robotic mapping is also described, along with an extensive list of open research problems.