Multi-Sensor Fusion for Motion Estimation in Visually-Degraded Environments

2019 ◽  
Author(s):  
Mikhail Sizintsev ◽  
Abhinav Rajvanshi ◽  
Han-Pang Chiu ◽  
Kevin Kaighn ◽  
Supun Samarasekera ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Sensor Fusion

Mobile robot ego-motion estimation by proprioceptive sensor fusion

2007 ◽  
Author(s):  
Jose-Luis Blanco ◽  
Javier Gonzalez ◽  
Juan-Antonio Fernandez-Madrigal
Keyword(s):  
Motion Estimation ◽  
Mobile Robot ◽  
Sensor Fusion

An Optimization-based Multi-Sensor Fusion Approach Towards Global Drift-Free Motion Estimation

2021 ◽  
pp. 1-1
Author(s):  
Ke Wang ◽  
Chuan Cao ◽  
Sai Ma ◽  
Fan Ren
Keyword(s):  
Motion Estimation ◽  
Sensor Fusion ◽  
Free Motion ◽  
Fusion Approach

scholarly journals An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1427 ◽  
Author(s):  
E. Shamwell ◽  
William Nothwang ◽  
Donald Perlis
Keyword(s):  
Motion Estimation ◽  
Sensor Fusion ◽  
Visual Motion ◽  
Deep Networks ◽  
Fusion Approach

scholarly journals A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 7
Author(s):  
Vicent Rodrigo Marco ◽  
Jens Kalkkuhl ◽  
Jörg Raisch ◽  
Thomas Seel
Keyword(s):  
Motion Estimation ◽  
Sensor Fusion ◽  
Calibration Method ◽  
Estimation Problem ◽  
Sensor Data ◽  
Measurement Unit ◽  
Coordinate Systems ◽  
Extrinsic Calibration ◽  
Adaptive Estimator ◽  
Precise Knowledge

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

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