Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors

In this paper, a drag model-aided fault-tolerant state estimation method is presented for quadrotors. Firstly, the drag model accuracy was improved by modeling an angular rate related item and an angular acceleration related item, which are related with flight maneuver. Then the drag model, light detection and ranging (LIDAR), and inertial measurement unit (IMU) were fused based on the Federal Kalman filter frame. In the filter, the LIDAR estimation fault was detected and isolated, and the disturbance to the drag model was estimated and compensated. Some experiments were carried out, showing that the velocity and position estimation were improved compared with the traditional LIDAR/IMU fusion scheme.

Section: Drag Model-lidar-imu Fusion Schemementioning

confidence: 99%

A Drag Model-LIDAR-IMU Fault-Tolerance Fusion Method for Quadrotors

Lyu

Wang

Lai

et al. 2019

“…Because of the aperture problem [ 19 ], only the measurement components which are orthogonal to the transferred line can be used for correction. In [ 3 , 17 ], the line-point is chosen as observation, which is defined as the closest point on the line segment to the image origin. Accordingly, the error function is defined as the differences between the measured and transferred line-points, which is similar to the error function of point features.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…However, inertial navigation systems (INS) are proved to drift over time due to error accumulation [ 1 ]. In the last decades, the topic of vision-aided inertial navigation has received considerable attention in the research community, thanks to some important advantages [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Firstly, the integrated system can operate in environments where GPS is unreliable or unavailable.…”

Section: Introductionmentioning

confidence: 99%

Tightly-Coupled Stereo Visual-Inertial Navigation Using Point and Line Features

Kong

Zhang

et al. 2015

Self Cite

This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone.

“…In [3] a matrix Kalman filter (MKF) using as inputs several on-board sensors has been implemented for an integrated indoor navigation system based on a 4 wheels robotic platform. The MKF rearranges the original nonlinear process model in a pseudo-linear process model.…”

Section: Papers In the Special Issuementioning

confidence: 99%

Introduction to the Special Issue on “New Trends towards Automatic Vehicle Control and Perception Systems”

Milanés

Bergasa

2013

Intelligent and automatic systems are making our daily life easier. They are able to automate tasks that, up to now, were performed by humans, freeing them from these tedious tasks. They are mainly based on the classical robotic architectures where the stages of perception-using different sensor sources or even a fusion of a set of them-and planning-where intelligent control systems are applied-play a key role. Among all of the fields in which intelligent systems can be applied, transport systems are considered one of the most promising ones since over one million fatalities-including drivers, pedestrians, cyclists and motorcyclists-are registered each year worldwide and they can definitively help to reduce these figures.The growth in the number of drivers in the last decades, and consequently in the number of vehicles, has made traffic accidents become a major concern in the road transportation sector. Solutions such as building new transport infrastructures-specifically in urban areas where space limitations make it almost impossible-or increasing fines for inappropriate driving have not achieved the goal of reducing the number of fatalities, either in urban environments or highways. New policies on the part of the Governments are focused on the development of a safer, more reliable and more efficient transportation supported by intelligent systems.The field of research in which all these topics fall into is well-known as intelligent transportation systems (ITS). ITS focuses on improving road safety by acting on two possible aspects: on the one hand, the infrastructure and the possibilities of optimizing traffic flow, and on the other hand, acting on the cars with the development of Advanced Driver Assistance Systems (ADAS) and the possibilities of optimizing traffic safety. The final goal is trying to reduce the number of collisions, or at least to mitigate their consequences. In this connection, one of the currently hot topics in the road OPEN ACCESS