相关论文: EKF-Based Radar-Inertial Odometry with Online Temp…
Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states,…
In many applications of biotechnology, measurements are available at different sampling rates, e.g., due to online sensors and offline lab analysis. Offline measurements typically involve time delays that may be unknown a priori due to the…
The accuracy of Earth system models is compromised by unknown and/or unresolved dynamics, making the quantification of systematic model errors essential. While a model parameter estimation, which allows parameters to change…
High-frequency and accurate state estimation is crucial for biped robots. This paper presents a tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) for biped robot state estimation based on an iterated extended Kalman filter. Beyond…
In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for…
Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search & rescue, a flying robot requires robust real-time perception, enabled by complementary sensors. IMU data constrains acceleration and rotation,…
In this work, we propose an approach for extrinsic sensor calibration from per-sensor ego-motion estimates. Our problem formulation is based on dual quaternions, enabling two different online capable solving approaches. We provide a…
This paper introduces a novel GPS-aided visual-wheel odometry (GPS-VWO) for ground robots. The state estimation algorithm tightly fuses visual, wheeled encoder and GPS measurements in the way of Multi-State Constraint Kalman Filter (MSCKF).…
High-resolution temperature monitoring is essential for many engineering and scientific applications, but conventional sensors are limited by insufficient resolution and susceptibility to electromagnetic interference. Fiber-optic…
In the Internet of Things (IoT) paradigm, distributed sensors and actuators can observe and act on their environment, communicating wirelessly. In this context, filtering the observations and tracking the network and environment state over…
Radar ensures robust sensing capabilities in adverse weather conditions, yet challenges remain due to its high inherent noise level. Existing radar odometry has overcome these challenges with strategies such as filtering spurious points,…
While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are…
Enabling bistatic radar sensing within the context of integrated sensing and communication (ISAC) for future sixth generation mobile networks demands strict synchronization accuracy, which is particularly challenging to be achieved with…
This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended…
This paper presents a fast lidar-inertial odometry (LIO) that is robust to aggressive motion. To achieve robust tracking in aggressive motion scenes, we exploit the continuous scanning property of lidar to adaptively divide the full scan…
In this paper, we propose an Invariant Extended Kalman Filter (IEKF) based Visual-Inertial Odometry (VIO) using multiple features in man-made environments. Conventional EKF-based VIO usually suffers from system inconsistency and angular…
While camera and LiDAR are widely used in most of the assisted and autonomous driving systems, only a few works have been proposed to associate the temporal synchronization and extrinsic calibration for camera and LiDAR which are dedicated…
Autonomous vehicles (AVs) fuse data from multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in sensor fusion; although radar…
In GNSS-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation crucial for accurate state estimation. However, the severe communication…
The extended Kalman filter (EKF) is a widely adopted method for sensor fusion in navigation applications. A crucial aspect of the EKF is the online determination of the process noise covariance matrix reflecting the model uncertainty. While…