A self-contained autonomous dead reckoning (DR) system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer-aided inertial navigation system (ODO/INS) is a classical solution. In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the odometer, and further, investigate three types of measurement models, including the velocity measurement, displacement increment measurement, and contact point zero-velocity measurement, in the Wheel-IMU based DR system. The measurement produced by the Wheel-IMU along with the non-holonomic constraint (NHC) are fused with INS through an error-state extended Kalman filter (EKF). Theoretical discussion and field tests illustrate the feasibility and equivalence of the three measurements in terms of the overall DR performance. The maximum horizontal position drifts are all less than 2% of the total travelled distance. Additionally, the displacement increment measurement model is less sensitive to the lever arm error between the Wheel-IMU and the wheel center.
To improve the accuracy and robustness of the inertial navigation systems (INS) for wheeled robots without adding additional component cost, we propose Wheel-INS, a complete dead reckoning solution based on a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU). There are two major advantages by mounting an IMU to the center of a non-steering wheel of the ground vehicle. Firstly, the gyroscope outputs can be used to calculate the wheel speed, so as to replace the traditional odometer to mitigate the error drift of INS. Secondly, with the rotation of the wheel, the…
A reliable self-contained navigation system is essential for autonomous vehicles. Based on our previous study on Wheel-INS \cite{niu2019}, a wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR) system, in this paper, we propose a multiple IMUs-based DR solution for the wheeled robots. The IMUs are mounted at different places of the wheeled vehicles to acquire various dynamic information. In particular, at least one IMU has to be mounted at the wheel to measure the wheel velocity and take advantages of the rotation modulation. The system is implemented through a distrib…
MINES ParisTech, PSL Research University（巴黎高等矿业学院，巴黎文理研究大学）；Safran Tech, Groupe Safran（赛峰集团赛峰科技）
In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometr…
We present a dead reckoning strategy for increased resilience to position estimation failures on multirotors, using only data from a low-cost IMU and novel, bio-inspired airflow sensors. The goal is challenging, since low-cost IMUs are subject to large noise and drift, while 3D airflow sensing is made difficult by the interference caused by the propellers and by the wind. Our approach relies on a deep-learning strategy to interpret the measurements of the bio-inspired sensors, a map of the wind speed to compensate for position-dependent wind, and a filter to fuse the information and generate a…
MINES ParisTech, PSL Research University（巴黎矿业学院，巴黎文理研究大学）；University of New Caledonia（新喀里多尼亚大学）；Safran Tech（赛峰科技）
This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences. The obtained performances are achieved thanks to a well-chosen model, a proper loss function for orientation increments, and through the identification of key points when training with high-frequency inertial data. Our approach builds upon a neural network based on dilated convolutions, without requir…
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU
Department of Engineering ``Enzo Ferrari'', University of Modena（工程系（恩佐·费拉里），莫德纳大学）；Department of Science and Methods for Engineering, University of Modena（工程科学与方法系，莫德纳大学）
This paper presents a deep learning approach to aid dead-reckoning (DR) navigation using a limited sensor suite. A Recurrent Neural Network (RNN) was developed to predict the relative horizontal velocities of an Autonomous Underwater Vehicle (AUV) using data from an IMU, pressure sensor, and control inputs. The RNN network is trained using experimental data, where a doppler velocity logger (DVL) provided ground truth velocities. The predictions of the relative velocities were implemented in a dead-reckoning algorithm to approximate north and east positions. The studies in this paper were twofo…
Spiking Neural-Invariant Kalman Fusion for Accurate Localization Using Low-Cost IMUs
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；East China Normal University（东华大学）；Innovation and Research and Development Department, BoardWare Information System Co.Ltd（BoardWare信息系统有限公司创新与研发部）
BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams
Dept of Computer and Information Science, University of Konstanz, Germany（计算机与信息科学系，康斯坦茨大学，德国）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany（集体行为高级研究中心，康斯坦茨大学，德国）；Dept of Aerospace Engineering, Indian Institute of Science, Bangalore, India（航空航天工程系，印度科学研究院，班加罗尔，印度）
Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU
University of Bonn（波恩大学）；Institute of Geodesy and Geoinformation（大地测量与地理信息研究所）；Center for Robotics（机器人中心）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；GNSS Research Center（全球导航卫星系统研究中心）；Wuhan University（武汉大学）
In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process (GP) model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter (RBPF) to cor…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-bas…
CREVE: An Acceleration-based Constraint Approach for Robust Radar Ego-Velocity Estimation
Department of Intelligent Mechatronics Engineering（智能机电工程系）；Department of Convergence Engineering for Intelligent Drone（智能无人机融合工程系）；Department of Artificial Intelligence and Robotics（人工智能与机器人学系）；AIRI（智能机器人研究院）；Sejong University（世宗大学）
Applications of inertial measurement units are extremely diverse, and are expected to see a further increase in number due to current trends in robotics as well as recent advances in Micro Electromechanical sensors (MEMS). The traditional method of inertial measurement has depended on costly, power-intensive, error-prone Inertial Measurement Units (IMUs) that represent a single point of failure. Promising areas of current research include methods for combining multiple redundant sensors, which collectively provide more accurate and more dependable estimates of state, and wholly new IMU layouts…
GNSS Research Center, Wuhan University（武汉大学卫星导航定位技术研究中心）
Inertial measurement unit (IMU) preintegration is widely used in factor graph optimization (FGO); e.g., in visual-inertial navigation system and global navigation satellite system/inertial navigation system (GNSS/INS) integration. However, most existing IMU preintegration models ignore the Earth's rotation and lack delicate integration processes, and these limitations severely degrade the INS accuracy. In this study, we construct a refined IMU preintegration model that incorporates the Earth's rotation, and analytically compute the covariance and Jacobian matrix. To mitigate the impact caused …
To obtain a high-accuracy position with SINS(Strapdown Inertial Navigation System), initial alignment needs to determine initial attitude rapidly and accurately. High-accuracy grade IMU(Inertial Measurement Uint) can obtain the initial attitude indenpendently, however, the low-accuracy grade gyroscope doesn't adapt to determine the heading angle, hence the initial attitude matrix will not be obtained. If using large misalignment angle model to estiamting heading angle, the convergence time will become much longer. For solving these two problems, a novel alignment algorithm combined backtrackin…
Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Hubei Luojia Laboratory, Wuhan University（湖北珞珈实验室）；Chinese Antarctic Center of Surveying and Mapping, Wuhan University（中国极地测绘南极科考中心）
Building upon the theory of Kalman Filtering on Lie Groups, this paper describes an Extended Kalman Filter and Smoother for Loosely Coupled Integration of GNSS/INS tailored for post-processing applications. The approach employs a dynamic model on a matrix Lie Group that aggregates position, velocity, attitude, and the IMU biases as a single element of a Lie group. The development was motivated by a drone-borne Differential Interferometric SAR (DinSAR) application, which requires high-precision navigation information for short-flight missions using low-cost MEMS sensors. The filter and the Rauc…
