Simultaneous Parameter Calibration, Localization, and Mapping

Kümmerle, Rainer; Grisetti, Giorgio; Burgard, Wolfram

doi:10.1080/01691864.2012.728694

Cited by 38 publications

(11 citation statements)

References 29 publications

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“…A large range of algorithms [56,57,[63][64][65][66][67][68][69][70] have been developed to deal with the key issues in producing and maintaining a suitable metric map for robot navigation: sensor observation uncertainty, scalability to larger environments and the need to incorporate changes in the environment. Metric occupancy maps have been used extensively both in forming global representations of a robot's entire operating environment, as well Figure 3.…”

Section: World Representation (A) Robotic World Representations For Nmentioning

confidence: 99%

Principles of goal-directed spatial robot navigation in biomimetic models

Milford

Schulz

2014

Phil. Trans. R. Soc. B

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One contribution of 18 to a Theme Issue 'The principles of goal-directed decisionmaking: from neural mechanisms to computation and robotics'. Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goaldirected navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in 'real-world' environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

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Section: World Representation (A) Robotic World Representations For Nmentioning

confidence: 99%

Principles of goal-directed spatial robot navigation in biomimetic models

Milford

Schulz

2014

Phil. Trans. R. Soc. B

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“…Point correspondences found in sequences of images are the input data for a wide range of computer vision algorithms, including tracking [1,2], 3D reconstruction [3,4], image stitching [5], visual odometry [6,7], video surveillance [8,9] and simultaneous localization and mapping [10,11]. As the quality of the input data directly influences the final results produced by the aforementioned algorithms, numerous solutions to the problem of automated image feature extraction and matching have been proposed by the research community.…”

Section: Introductionmentioning

confidence: 99%

A High-Performance FPGA-Based Image Feature Detector and Matcher Based on the FAST and BRIEF Algorithms

Fularz

Kraft

Schmidt

et al. 2015

International Journal of Advanced Robotic Systems

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Image feature detection and matching is a fundamental operation in image processing. As the detected and matched features are used as input data for high-level computer vision algorithms, the matching accuracy directly influences the quality of the results of the whole computer vision system. Moreover, as the algorithms are frequently used as a part of a real-time processing pipeline, the speed at which the input image data are handled is also a concern. The paper proposes an embedded system architecture for feature detection and matching. The architecture implements the FAST feature detector and the BRIEF feature descriptor and is capable of establishing key point correspondences in the input image data stream coming from either an external sensor or memory at a speed of hundreds of frames per second, so that it can cope with most demanding applications. Moreover, the proposed design is highly flexible and configurable, and facilitates the trade-off between the processing speed and programmable logic resource utilization. All the designed hardware blocks are designed to use standard, widely adopted hardware interfaces based on the AMBA AXI4 interface protocol and are connected using an underlying direct memory access (DMA) architecture, enabling bottleneckfree inter-component data transfers.

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“…For example, the camera to IMU extrinsic calibration [17], [18], [19] and IMU to body frame calibration [20], can be successfully refined during online operation, provided the IMU experiences sufficient excitation. Kümmerle et al [21] present an approach that simultaneously performs calibration, mapping, and localization using a graph-based nonlinear least squares framework. Although accurate calibration performance was achieved, the mapping formulation includes information from a 3D laser scanner which greatly simplifies scale recovery for the solution.…”

Section: Related Workmentioning

confidence: 99%

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Choi

Rebello

Koppe

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Dynamic Camera Clusters (DCCs) are multicamera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.

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Simultaneous Parameter Calibration, Localization, and Mapping

Cited by 38 publications

References 29 publications

Principles of goal-directed spatial robot navigation in biomimetic models

Principles of goal-directed spatial robot navigation in biomimetic models

A High-Performance FPGA-Based Image Feature Detector and Matcher Based on the FAST and BRIEF Algorithms

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

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