Journal: The International Journal of Robotics Research

Abbreviation

Int. J. Rob. Res.

Publisher

SAGE

Journal Volumes

ISSN

0278-3649
1741-3176

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Publications 1 - 10 of 63
  • SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group
    Item type: Journal Article
    Rosen, David M.; Carlone, Luca; Bandeira, Afonso S.; et al. (2019)
    The International Journal of Robotics Research
  • Keyframe-based visual-inertial odometry using nonlinear optimization
    Item type: Journal Article
    Leutenegger, Stefan; Lynen, Simon; Bosse, Michael; et al. (2015)
    The International Journal of Robotics Research
  • Image and animation display with multiple mobile robots
    Item type: Journal Article
    Alonso-Mora, Javier; Breitenmoser, Andreas; Rufli, Martin; et al. (2012)
    The International Journal of Robotics Research
  • A framework for collaborative multi-robot mapping using spectral graph wavelets
    Item type: Journal Article
    Bernreiter, Lukas; Khattak, Shehryar Masaud Khan; Ott, Lionel; et al. (2024)
    The International Journal of Robotics Research
    The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift's origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.
  • Design and optimal control of a tiltrotor micro-aerial vehicle for efficient omnidirectional flight
    Item type: Journal Article
    Allenspach, Mike; Bodie, Karen; Brunner, Maximilian; et al. (2020)
    The International Journal of Robotics Research
    Omnidirectional micro-aerial vehicles (MAVs) are a growing field of research, with demonstrated advantages for aerial interaction and uninhibited observation. While systems with complete pose omnidirectionality and high hover efficiency have been developed independently, a robust system that combines the two has not been demonstrated to date. This paper presents the design and optimal control of a novel omnidirectional vehicle that can exert a wrench in any orientation while maintaining efficient flight configurations. The system design is motivated by the result of a morphology design optimization. A six-degree-of-freedom optimal controller is derived, with an actuator allocation approach that implements task prioritization, and is robust to singularities. Flight experiments demonstrate and verify the system's capabilities.
  • The EuRoC micro aerial vehicle datasets
    Item type: Journal Article
    Burri, Michael; Nikolic, Janosch; Gohl, Pascal; et al. (2016)
    The International Journal of Robotics Research
  • Robust collaborative object transportation using multiple MAVs
    Item type: Journal Article
    Tagliabue, Andrea; Kamel, Mina; Siegwart, Roland; et al. (2019)
    The International Journal of Robotics Research
  • Bayesian iterative closest point for mobile robot localization
    Item type: Journal Article
    Maken, Fahira Afzal; Ramos, Fabio; Ott, Lionel (2022)
    The International Journal of Robotics Research
    Accurate localization of a robot in a known environment is a fundamental capability for successfully performing path planning, manipulation, and grasping tasks. Particle filters, also known as Monte Carlo localization (MCL), are a commonly used method to determine the robot's pose within its environment. For ground robots, noisy wheel odometry readings are typically used as a motion model to predict the vehicle's location. Such a motion model requires tuning of various parameters based on terrain and robot type. However, such an ego-motion estimation is not always available for all platforms. Scan matching using the iterative closest point (ICP) algorithm is a popular alternative approach, providing ego-motion estimates for localization. Iterative closest point computes a point estimate of the transformation between two poses given point clouds captured at these locations. Being a point estimate method, ICP does not deal with the uncertainties in the scan alignment process, which may arise due to sensor noise, partial overlap, or the existence of multiple solutions. Another challenge for ICP is the high computational cost required to align two large point clouds, limiting its applicability to less dynamic problems. In this paper, we address these challenges by leveraging recent advances in probabilistic inference. Specifically, we first address the run-time issue and propose SGD-ICP, which employs stochastic gradient descent (SGD) to solve the optimization problem of ICP. Next, we leverage SGD-ICP to obtain a distribution over transformations and propose a Markov Chain Monte Carlo method using stochastic gradient Langevin dynamics (SGLD) updates. Our ICP variant, termed Bayesian-ICP, is a full Bayesian solution to the problem. To demonstrate the benefits of Bayesian-ICP for mobile robotic applications, we propose an adaptive motion model employing Bayesian-ICP to produce proposal distributions for Monte Carlo Localization. Experiments using both Kinect and 3D LiDAR data show that our proposed SGD-ICP method achieves the same solution quality as standard ICP while being significantly more efficient. We then demonstrate empirically that Bayesian-ICP can produce accurate distributions over pose transformations and is fast enough for online applications. Finally, using Bayesian-ICP as a motion model alleviates the need to tune the motion model parameters from odometry, resulting in better-calibrated localization uncertainty.
  • Risk-aware graph search with dynamic edge cost discovery
    Item type: Journal Article
    Chung, Jen Jen; Smith, Andrew J.; Skeele, Ryan; et al. (2019)
    The International Journal of Robotics Research
  • The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM
    Item type: Journal Article
    Mueggler, Elias; Rebecq, Henri; Gallego, Guillermo; et al. (2017)
    The International Journal of Robotics Research
Publications 1 - 10 of 63