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Relevant bibliographies by topics / Robocentric

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Academic literature on the topic 'Robocentric'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Robocentric"

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Castellanos, J. A., R. Martinez-Cantin, J. D. Tardós, and J. Neira. "Robocentric map joining: Improving the consistency of EKF-SLAM." Robotics and Autonomous Systems 55, no. 1 (January 2007): 21–29. http://dx.doi.org/10.1016/j.robot.2006.06.005.

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Lorente, María-Teresa, Eduardo Owen, and Luis Montano. "Model-based robocentric planning and navigation for dynamic environments." International Journal of Robotics Research 37, no. 8 (June 25, 2018): 867–89. http://dx.doi.org/10.1177/0278364918775520.

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This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented.

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Biswas, Srijanee, and Rajnikant Sharma. "Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle." Journal of Aerospace Information Systems 18, no. 8 (August 2021): 488–505. http://dx.doi.org/10.2514/1.i010843.

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Bonin-Font, Francisco, Antoni Burguera, Alberto Ortiz, and Gabriel Oliver. "A monocular mobile robot reactive navigation approach based on the inverse perspective transformation." Robotica 31, no. 2 (May 22, 2012): 225–49. http://dx.doi.org/10.1017/s0263574712000252.

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SUMMARYThis paper presents an approach to visual obstacle avoidance and reactive robot navigation for outdoor and indoor environments. The obstacle detection algorithm includes an image feature tracking procedure followed by a feature classification process based on the IPT (Inverse Perspective Transformation). The classifier discriminates obstacle points from ground points. Obstacle features permit to draw out the obstacle boundaries which are used to construct a local and qualitative polar occupancy grid, analogously to a visual sonar. The navigation task is completed with a robocentric localization algorithm to compute the robot pose by means of an EKF (Extended Kalman Filter). The filter integrates the world coordinates of the ground points and the robot position in its state vector. The visual pose estimation process is intended to correct possible drifts on the dead-reckoning data provided by the proprioceptive robot sensors. The experiments, conducted indoors and outdoors, illustrate the range of scenarios where our proposal has proved to be useful, and show, both qualitatively and quantitatively, the benefits it provides.

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Bloesch, Michael, Michael Burri, Sammy Omari, Marco Hutter, and Roland Siegwart. "Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback." International Journal of Robotics Research 36, no. 10 (September 2017): 1053–72. http://dx.doi.org/10.1177/0278364917728574.

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This paper presents a visual-inertial odometry framework that tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter. By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of noncorner-shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach, which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it with other state-of-the-art visual-inertial frameworks. Experimental results show that robust localization with high accuracy can be achieved with this filter-based framework.

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Koch, Daniel P., David O. Wheeler, Randal W. Beard, Timothy W. McLain, and Kevin M. Brink. "Relative multiplicative extended Kalman filter for observable GPS-denied navigation." International Journal of Robotics Research 39, no. 9 (June 23, 2020): 1085–121. http://dx.doi.org/10.1177/0278364920903094.

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This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations.

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Huai, Zheng, and Guoquan Huang. "Robocentric visual–inertial odometry." International Journal of Robotics Research, July 18, 2019, 027836491985336. http://dx.doi.org/10.1177/0278364919853361.

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In this paper, we propose a novel robocentric formulation of the visual–inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual–inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a six-axis inertial measurement unit (IMU). The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global pose. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterparts that has been identified in the literature as the main cause of estimation inconsistency. Furthermore, we investigate in depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated for by the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively validated through both Monte Carlo simulation and real-world experiments with different sensing platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy, and efficiency.

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Dissertations / Theses on the topic "Robocentric"

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Holmberg, Max. "Development and Evaluation of a Robocentric SLAM Algorithm." Thesis, Uppsala universitet, Signaler och System, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-366772.

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In this master thesis the front end of a Simultaneous Localization And Mapping(SLAM) system is developed in the programming language C++ and themeta-operating system ROS (Robot Operating System). The algorithms are based onprevious work done at the Swedish defense research agency (FOI) and are a part of aGPS free positioning system developed for military use. The parts that have beenimplemented during the project includes: feature extraction from LIDAR data, featureassociation and a Robocentric Extended Kalman Filter. The sensors used in the SLAMsystem are a Velodyne LIDAR (LIght Detection And Ranging) unit and an IMU (InertialMeasurement Unit). During the master thesis, data collection has been done indifferent types of outdoor environments. The resulting front end SLAM with a Kalmanfilter is evaluated in the different types of environments and compared with bothaccurate RTK (Real Time Kinetic) GPS and a version of the filter that uses datafiltered with a GPS. The GPS free SLAM algorithm in urban and forest environmentsgives position estimates that drifts less than 2% compared with the SLAM algorithmthat has help from a GPS. In open field terrain the GPS free SLAM algorithm hastrouble estimating its position due to a lack of features, which results in significantdrift over time. When the SLAM algorithm with GPS filtered data is compared with anaccurate Real Time Kinetic GPS in an urban environment the average drift is less than1%.

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Biswas, Srijanee. "Goal-Aware Robocentric Mapping and Navigation of a Quadrotor Unmanned Aerial Vehicle." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552581467878839.

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Ferrin, Jeffrey L. "Autonomous Goal-Based Mapping and Navigation Using a Ground Robot." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6190.

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Ground robotic vehicles are used in many different applications. Many of these uses include tele-operation of the robot. This allows the robot to be deployed in locations that are too difficult or are unsafe for human access. The ability of a ground robot to autonomously navigate to a desired location without a-priori map information and without using GPS would allow robotic vehicles to be used in many of these situations and would free the operator to focus on other more important tasks. The purpose of this research is to develop algorithms that enable a ground robot to autonomously navigate to a user-selected location. The goal is selected from a video feed from the robot and the robot drives to the goal location while avoiding obstacles. The method uses a monocular camera for measuring the locations of the goal and landmarks. The method is validated in simulation and through experiments on an iRobot Packbot platform. A novel goal-based robocentric mapping algorithm is derived in Chapter 3. This map is created using an extended Kalman filter (EKF) by tracking the position of the goal along with other available landmarks surrounding the robot as it drives towards the goal. The mapping is robocentric, meaning that the map is a local map created in the robot-body frame. A unique state definition of the goal states and additional landmarks is presented that improves the estimate of the goal location. An improved 3D model is derived and used to allow the robot to drive on non-flat terrain while calculating the position of the goal and other landmarks. The observability and consistency of the proposed method are shown in Chapter 4. The visual tracking algorithm is explained in Chapter 5. This tracker is used with the EKF to improve tracking performance and to allow the objects to be tracked even after leaving the camera field of view for significant periods of time. This problem presents a difficult challenge for visual tracking because of the drastic change in size of the goal object as the robot approaches the goal. The tracking method is validated through experiments in real-world scenarios. The method of planning and control is derived in Chapter 6. A Model Predictive Control (MPC) formulation is designed that explicitly handles the sensor constraints of a monocular camera that is rigidly mounted to the vehicle. The MPC uses an observability-based cost function to drive the robot along a path that minimizes the position error of the goal in the robot-body frame. The MPC algorithm also avoids obstacles while driving to the goal. The conditions are explained that guarantee the robot will arrive within some specified distance of the goal. The entire system is implemented on an iRobot Packbot and experiments are conducted and presented in Chapter 7. The methods described in this work are shown to work on actual hardware allowing the robot to arrive at a user-selected goal in real-world scenarios.

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Wheeler, David Orton. "Relative Navigation of Micro Air Vehicles in GPS-Degraded Environments." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6609.

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Most micro air vehicles rely heavily on reliable GPS measurements for proper estimation and control, and therefore struggle in GPS-degraded environments. When GPS is not available, the global position and heading of the vehicle is unobservable. This dissertation establishes the theoretical and practical advantages of a relative navigation framework for MAV navigation in GPS-degraded environments. This dissertation explores how the consistency, accuracy, and stability of current navigation approaches degrade during prolonged GPS dropout and in the presence of heading uncertainty. Relative navigation (RN) is presented as an alternative approach that maintains observability by working with respect to a local coordinate frame. RN is compared with several current estimation approaches in a simulation environment and in hardware experiments. While still subject to global drift, RN is shown to produce consistent state estimates and stable control. Estimating relative states requires unique modifications to current estimation approaches. This dissertation further provides a tutorial exposition of the relative multiplicative extended Kalman filter, presenting how to properly ensure observable state estimation while maintaining consistency. The filter is derived using both inertial and body-fixed state definitions and dynamics. Finally, this dissertation presents a series of prolonged flight tests, demonstrating the effectiveness of the relative navigation approach for autonomous GPS-degraded MAV navigation in varied, unknown environments. The system is shown to utilize a variety of vision sensors, work indoors and outdoors, run in real-time with onboard processing, and not require special tuning for particular sensors or environments. Despite leveraging off-the-shelf sensors and algorithms, the flight tests demonstrate stable front-end performance with low drift. The flight tests also demonstrate the onboard generation of a globally consistent, metric, and localized map by identifying and incorporating loop-closure constraints and intermittent GPS measurements. With this map, mission objectives are shown to be autonomously completed.

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Conference papers on the topic "Robocentric"

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Schmuck, Viktor, Tingran Sheng, and Oya Celiktutan. "Robocentric Conversational Group Discovery." In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2020. http://dx.doi.org/10.1109/ro-man47096.2020.9223570.

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Huai, Zheng, and Guoquan Huang. "Robocentric Visual-Inertial Odometry." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593643.

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Schmuck, Viktor, and Oya Celiktutan. "RICA: Robocentric Indoor Crowd Analysis Dataset." In UKRAS20 Conference: “Robots into the real world”. EPSRC UK-RAS Network, 2020. http://dx.doi.org/10.31256/io1sq2r.

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Pinies, Pedro, Juan Tardos, and Jose Neira. "Localization of avalanche victims using robocentric SLAM." In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.282247.

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Bishop, A. N., and P. Jensfelt. "A stochastically stable solution to the problem of robocentric mapping." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152424.

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Owen, Eduardo, and Luis Montano. "A Robocentric Motion Planner for Dynamic Environments Using the Velocity Space." In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.282012.

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Burguera, A., Y. Gonzalez, and G. Oliver. "Underwater SLAM with robocentric trajectory using a mechanically scanned imaging sonar." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6094850.

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Burguera, A., Y. Gonzalez, and G. Oliver. "Underwater SLAM with robocentric trajectory using a Mechanically Scanned Imaging Sonar." In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). IEEE, 2011. http://dx.doi.org/10.1109/iros.2011.6048431.

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Boberg, Anders, Adrian N. Bishop, and Patric Jensfelt. "Robocentric mapping and localization in modified spherical coordinates with bearing measurements." In 2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2009. http://dx.doi.org/10.1109/issnip.2009.5416769.

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Bonin-Font, F., A. Burguera, A. Ortiz, and G. Oliver. "Combining obstacle avoidance with robocentric localization in a reactive visual navigation task." In 2012 IEEE International Conference on Industrial Technology (ICIT 2012). IEEE, 2012. http://dx.doi.org/10.1109/icit.2012.6209907.

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