This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The state prediction makes use of IMU readings while the state update relies directly on photometric feedback as measurements. Unlike feature-based methods, the photometric difference for the innovation term renders an inherent and robust data association process in a single step. The proposed approach is validated using real-world datasets. The results show that the proposed method offers better robustness, accuracy, and efficiency than a feature-based approach. Further investigation suggests that the accuracy of the flight velocity estimates from the proposed approach is comparable to those of two state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is $\approx15-30$ times faster thanks to the omission of reconstruction and mapping.
This paper presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various …
Iterated Invariant EKF for Quadruped Robot Odometry
Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia（动态腿系统实验室，意大利技术研究院）；Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学机械工程系）；Department of Electrical Engineering and Computer Science, University of Liège（列日大学电气工程与计算机科学系）；Mines Paris PSL, Centre for Robotics（巴黎综合理工学院机器人中心）
Depth estimation from stereo images remains a challenge even though studied for decades. The KITTI benchmark shows that the state-of-the-art solutions offer accurate depth estimation, but are still computationally complex and often require a GPU or FPGA implementation. In this paper we aim at increasing the accuracy of depth map estimation and reducing the computational complexity by using information from previous frames. We propose to transform the disparity map of the previous frame into the current frame, relying on the estimated ego-motion, and use this map as the prediction for the Kalma…
Kalman filter is presumably one of the most important and extensively used filtering techniques in modern control systems. Yet, nearly all current variants of Kalman filters are formulated in the Euclidean space $\mathbb{R}^n$, while many real-world systems (e.g., robotic systems) are really evolving on manifolds. In this paper, we propose a method to develop Kalman filters for such on-manifold systems. Utilizing $\boxplus$, $\boxminus$ operations and further defining an oplus operation on the respective manifold, we propose a canonical representation of the on-manifold system. Such a canonica…
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Vector Institute（向量研究所）
Visual-inertial odometry (VIO) systems traditionally rely on filtering or optimization-based techniques for egomotion estimation. While these methods are accurate under nominal conditions, they are prone to failure during severe illumination changes, rapid camera motions, or on low-texture image sequences. Learning-based systems have the potential to outperform classical implementations in challenging environments, but, currently, do not perform as well as classical methods in nominal settings. Herein, we introduce a framework for training a hybrid VIO system that leverages the advantages of l…
Iterated Invariant EKF for 3D Landmark-Aided Inertial Navigation
Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil（里约热内卢天主教大学）；Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia, Genova, Italy（动态腿部系统实验室，意大利理工学院）
Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization
Tampere University（坦佩雷大学）；Chalmers University of Technology（查尔姆斯理工大学）
5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In thi…
Uncertainty-Driven Radar-Inertial Fusion for Instantaneous 3D Ego-Velocity Estimation
Faculty of Engineering and Natural Sciences, Tampere University（工程与自然科学学院，塔尔皮奥大学）；Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通信科学学院，塔尔皮奥大学）；Engineering and Natural Sciences, Tampere University（工程与自然科学，塔尔皮奥大学）
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane no…
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction
Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；Huazhong University of Science and Technology（华中科技大学）
Efficient Invariant Kalman Filter for Inertial-based Odometry with Large-sample Environmental Measurements
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Mechatronic Engineering and Automation, Shanghai University（上海大学机电工程与自动化学院）；Department of Automation, East China University of Science and Technology（华东理工大学自动化系）
The fusion of camera sensor and inertial data is a leading method for ego-motion tracking in autonomous and smart devices. State estimation techniques that rely on non-linear filtering are a strong paradigm for solving the associated information fusion task. The de facto inference method in this space is the celebrated extended Kalman filter (EKF), which relies on first-order linearizations of both the dynamical and measurement model. This paper takes a critical look at the practical implications and limitations posed by the EKF, especially under faulty visual feature associations and the pres…
The Hong Kong University of Science and Technology（香港科技大学）
We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving lo…
In this letter, we propose a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses measurement from LiDAR, inertial sensor, and visual camera to achieve robust and accurate state estimation. Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization. To guarantee real-time performance, we estimate the state within the framework of error-state iterated Kalman-filter, and further improve the overall precision with our factor graph optimization. Taking advantage of measurement from all individual sensors, our algorithm is robust…
Recently, learning-based ego-motion estimation approaches have drawn strong interest from studies mostly focusing on visual perception. These groundbreaking works focus on unsupervised learning for odometry estimation but mostly for visual sensors. Compared to images, a learning-based approach using Light Detection and Ranging (LiDAR) has been reported in a few studies where, most often, a supervised learning framework is proposed. In this paper, we propose a novel approach to geometry-aware deep LiDAR odometry trainable via both supervised and unsupervised frameworks. We incorporate the Itera…
EKF-Based Radar-Inertial Odometry with Online Temporal Calibration
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST)（机械工程系，乌山国家科学技术研究院）；Department of Electrical and Electronic Engineering, Imperial College London（电气与电子工程系，伦敦帝国理工学院）
RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry
Department of Electrical Engineering, Linköping University（Linköping大学电子工程系）；Robotic Systems Lab (RSL), ETH Zurich（ETH Zurich机器人系统实验室）；Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen（Groningen大学伯努利学院）
Omni-LIVO: Robust RGB-Colored Multi-Camera Visual-Inertial-LiDAR Odometry via Photometric Migration and ESIKF Fusion
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；Advanced Laser Technology Lab of Anhui Province（安徽省先进激光技术实验室）；Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences（中国科学院上海技术物理研究所空间主动光电技术重点实验室）
FAST-LIVGO: A Degeneracy-Robust LiDAR-Inertial-Visual-GNSS Fusion Odometry
College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）；Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；College of Automation, Harbin Engineering University（哈尔滨工程大学自动化学院）
This paper presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly-coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and hence increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adap…
PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of California, Berkeley（加州大学伯克利分校）；Xinchen Qihang Co., Ltd.（新晨启航有限公司）
Learning-based visual ego-motion estimation is promising yet not ready for navigating agile mobile robots in the real world. In this article, we propose CUAHN-VIO, a robust and efficient monocular visual-inertial odometry (VIO) designed for micro aerial vehicles (MAVs) equipped with a downward-facing camera. The vision frontend is a content-and-uncertainty-aware homography network (CUAHN) that is robust to non-homography image content and failure cases of network prediction. It not only predicts the homography transformation but also estimates its uncertainty. The training is self-supervised, …
FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement
School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology（中国矿业大学江苏省智能采矿装备协同创新中心）；National Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology（中国矿业大学智能采矿装备技术全国重点实验室）
HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
College of Intelligent Systems Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能系统科学与工程学院）；National Innovation Institute of Defense Technology, Academy of Military Sciences China（中国军事科学院国防科技创新研究院）；Tianjin Artificial Intelligence Innovation Center (TAIIC)（天津人工智能创新中心）
KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry
School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）
