检索结果-内蒙古大学图书馆

Global Minimum Energy State Estimation for Embedded Nonlinear systems with Symmetry

学校读者我要写书评

暂无评论

Global Minimum Energy State Estimation for Embedded Nonlinea...

IEEE Conference on Decision and Control

作者： Pieter Van Goor Robert Mahony Robotics and Mechatronics (RaM) Group EEMCS Faculty University of Twente Enschede Netherlands System Theory and Robotics Lab Australian National University Canberra Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Manifolds Simulation Quaternions Measurement uncertainty Estimation Observers Particle measurements Energy states Kalman filters Nonlinear systems

An Equivariant Approach to Robust State Estimation for the ArduPilot Autopilot system

学校读者我要写书评

暂无评论

An Equivariant Approach to Robust State Estimation for the A...

IEEE International Conference on robotics and Automation (ICRA)

作者： Alessandro Fornasier Yixiao Ge Pieter van Goor Martin Scheiber Andrew Tridgell Robert Mahony Stephan Weiss Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The majority of commercial and open-source autopilot software for uncrewed aerial vehicles rely on the tried and tested extended Kalman filter (EKF) to provide the state estimation solution for the inertial navigation system (INS). While modern implementations achieve remarkable robustness, it is often due to the careful implementation of exception code for a multitude of corner cases along with significant skilled tuning effort. In this paper, we use the data wealth of the ArduPilot community to identify and highlight the most common real-world challenges in INS state estimation, including sensor self-calibration, robustness in static conditions, global navigation satellite system (GNSS) outliers and shifts, and robustness to faulty inertial measurement units (IMUs). We propose a novel equivariant filter (EqF) formulation for the INS solution that exploits a Semi-Direct-Bias symmetry group for multi-sensor fusion with self-calibration capabilities and incorporates equivariant velocity-type measurements. We augment the filter with a simple innovation-covariance inflation strategy that seamlessly handles GNSS outliers and shifts without requiring coding of a whole set of exception cases. We use real-world data from the Ardupilot community to demonstrate the performance of the proposed filter on known cases where existing filters fail without careful exception handling or case-specific tuning and benchmark against the ArduPilot’s EKF3, the most sophisticated EKF implementation currently available.

关键词： Global navigation satellite system Low-pass filters Inertial navigation Robot sensing systems Autonomous aerial vehicles Robustness Software

Equivariant IMU Preintegration with Biases: a Galilean Group Approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Delama, Giulio Fornasier, Alessandro Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation system (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs, we derive a discrete-time formulation of the IMU preintegration on Gal(3) ⋈ gal(3), the left-trivialization of the tangent group of the Galilean group Gal(3). We define a novel preintegration error that geometrically couples the navigation states and the bias leading to lower linearization error. Our method improves in consistency compared to existing preintegration approaches which treat IMU biases as a separate state-space. Extensive validation against state-of-the-art methods, both in simulation and with real-world IMU data, implementation in the Lie++ library, and open-source code are provided. Copyright © 2024, The Authors. All rights reserved.

关键词： Inertial navigation systems

MSCEqF: A Multi State Constraint Equivariant Filter for Vision-aided Inertial Navigation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Fornasier, Alessandro van Goor, Pieter Allak, Eren Mahony, Robert Weiss, Stephan The Control of Networked Systems Group University of Klagenfurt Austria The System Theory and Robotics Lab Australian National University Australia

This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint equivariant filter (MSCEqF, in analogy to the well known MSCKF). We define a symmetry group and corresponding group action that allow specifically the design of an equivariant filter for the problem of visual-inertial odometry (VIO) including IMU bias, and camera intrinsic and extrinsic calibration states. In contrast to state-of-the-art invariant extended Kalman filter (IEKF) approaches that simply tack IMU bias and other states onto the SE2(3) group, our filter builds upon a symmetry that properly includes all the states in the group structure. Thus, we achieve improved behavior, particularly when linearization points largely deviate from the truth (i.e., on transients upon state disturbances). Our approach is inherently consistent even during convergence phases from significant errors without the need for error uncertainty adaptation, observability constraint, or other consistency enforcing techniques. This leads to greatly improved estimator behavior for significant error and unexpected state changes during, e.g., long-duration missions. We evaluate our approach with a multitude of different experiments using three different prominent real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

Equivariant Symmetries for Inertial Navigation systems

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Fornasier, Alessandro Ge, Yixiao van Goor, Pieter Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian Centre for Robotic Vision Australian National University Australia

This paper investigates the problem of inertial navigation system (INS) filter design through the lens of symmetry. The extended Kalman filter (EKF) and its variants, have been the staple of INS filtering for 50 years;however, recent advances in inertial navigation systems have exploited matrix Lie group structure to design stochastic filters and state observers that have been shown to display superior performance compared to classical solutions. In this work, we consider the case where a vehicle has an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver. We show that all the modern variants of the EKF for these sensors can be interpreted as the recently proposed Equivariant Filter (EqF) design methodology applied to different choices of symmetry group for the INS problem. This leads us to propose two new symmetries for the INS problem that have not been considered in the prior literature, and provide a discussion of the relative strengths and weaknesses of all the different algorithms. We believe the collection of symmetries that we present here capture all the sensible choices of symmetry for this problem and sensor suite, and that the analysis provided is indicative of the relative real-world performance potential of the different algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Inertial navigation systems

Equivariant filter design for inertial navigation systems with input measurement biases

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Mahony, Robert Weiss, Stephan Control of Networked Systems Group University of Klagenfurt Austria System Theory and Robotics Lab Australian National University Australia

Inertial Navigation systems (INS) are a key technology for autonomous vehicles applications. Recent advances in estimation and filter design for the INS problem have exploited geometry and symmetry to overcome limitations of the classical Extended Kalman Filter (EKF) approach that formed the mainstay of INS systems since the mid-twentieth century. The industry standard INS filter, the Multiplicative Extended Kalman Filter (MEKF), uses a geometric construction for attitude estimation coupled with classical Euclidean construction for position, velocity and bias estimation. The recent Invariant Extended Kalman Filter (IEKF) provides a geometric framework for the full navigation states, integrating attitude, position and velocity, but still uses the classical Euclidean construction to model the bias states. In this paper, we use the recently proposed Equivariant Filter (EqF) framework to derive a novel observer for biased inertial-based navigation in a fully geometric framework. The introduction of virtual velocity inputs with associated virtual bias leads to a full equivariant symmetry on the augmented system. The resulting filter performance is evaluated with both simulated and realworld data, and demonstrates increased robustness to a wide range of erroneous initial conditions, and improved accuracy when compared with the industry standard Multiplicative EKF (MEKF) approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Inertial navigation systems

Overcoming Bias: Equivariant Filter Design for Biased Attitude Estimation with Online Calibration

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fornasier, Alessandro Ng, Yonhon Brommer, Christian Böhm, Christoph Mahony, Robert Weiss, Stephan The Control of Networked Systems Group University of Klagenfurt Austria The System Theory and Robotics Lab Australian National University Australia

Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system’s capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and reset) of such filters are of crucial importance in guaranteeing robust operation of autonomous systems. This paper introduces a new generic formulation for a gyroscope aided attitude estimator using N direction measurements including both body-frame and reference-frame direction type measurements. The approach is based on an integrated state formulation that incorporates navigation, extrinsic calibration for all direction sensors, and gyroscope bias states in a single equivariant geometric structure. This newly proposed symmetry allows modular addition of different direction measurements and their extrinsic calibration while maintaining the ability to include bias states in the same symmetry. The subsequently proposed filter-based estimator using this symmetry noticeably improves the transient response, and the asymptotic bias and extrinsic calibration estimation compared to state-of-the-art approaches. The estimator is verified in statistically representative simulations and is tested in real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

AirDOS: Dynamic SLAM benefits from Articulated Objects

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Qiu, Yuheng Wang, Chen Wang, Wenshan Henein, Mina Scherer, Sebastian The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States The System Theory and Robotics Lab Australian National University Australia

Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. Existing methods mainly focus on identifying and excluding dynamic objects from the optimization. In this paper, we show that feature-based visual SLAM systems can also benefit from the presence of dynamic articulated objects by taking advantage of two observations: (1) The 3D structure of each rigid part of articulated object remains consistent over time;(2) The points on the same rigid part follow the same motion. In particular, we present AirDOS, a dynamic object-aware system that introduces rigidity and motion constraints to model articulated objects. By jointly optimizing the camera pose, object motion, and the object 3D structure, we can rectify the camera pose estimation, preventing tracking loss, and generate 4D spatio-temporal maps for both dynamic objects and static scenes. Experiments show that our algorithm improves the robustness of visual SLAM algorithms in challenging crowded urban environments. To the best of our knowledge, AirDOS is the first dynamic object-aware SLAM system demonstrating that camera pose estimation can be improved by incorporating dynamic articulated objects. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

Biologically-inspired auditory perception during robotic bone milling

学校读者我要写书评

暂无评论

Biologically-inspired auditory perception during robotic bon...

2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Dai, Yu Xue, Yuan Zhang, Jianxun Li, Jianmin Institute of Robotics and Automatic Information System Nankai University Tianjin China Department of Orthopedic Surgery Tianj in Medical University General Hospital Tianjin China Key Lab for Mechanism Theory and Equipment Design Ministry of Education Tianjin University Tianjin China

ISBN: (纸本)9781509046331

Bone milling is widely used in many hard tissue surgical procedures, and the main concern is the mechanical damage to some important tissues induced by the high-speed rotating tool. In this study, the behavior of the human auditory system is analyzed. Following this, a commercially available microphone is mounted on the robot arm and then measures the sound generated from bone milling. Inspired by the bandpass filtering in the cochlea, the recorded sound pressure signal is decomposed into a set of subband signals by wavelet packet transform. Inspired by encoding and perception mechanisms in human auditory system, the average amplitude of the wavelet coefficients in each subband is calculated and inputted to a self-organizing feature map so as to classify different milling states. In order to increase the robustness to noise, the sum of the Manhattan distances between all winning neurons is calculated to select the optimum dimension of the map. The experimental results in milling in vitro porcine spines prove that the proposed method can determine which type of tissue is being cut when the suction noise exists, and the success classification rate is no less than 85%. Therefore, the safety of the robot-assisted milling surgery is improved. © 2017 IEEE.

关键词： Milling (machining)