scholarly journals Extrinsic calibration for motion estimation using unit quaternions and particle filtering

2020 ◽  
Vol 41 (3) ◽  
pp. 207-221
Author(s):  
Aksel Sveier ◽  
Torstein A. Myhre ◽  
Olav Egeland
Keyword(s):  
Motion Estimation ◽  
Particle Filtering ◽  
Extrinsic Calibration ◽  
Unit Quaternions

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.

scholarly journals A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2363 ◽  
Author(s):  
Zhongli Wang ◽  
Litong Fan ◽  
Baigen Cai
Keyword(s):  
Motion Estimation ◽  
Object Tracking ◽  
Relative Motion ◽  
Particle Filtering ◽  
Spatial Relations ◽  
Visual Object ◽  
Camera Motion ◽  
Motion Model ◽  
Unified Framework ◽  
Map Solution

Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relative-motion model into a sequential Bayesian framework to track multiple objects and the camera’s ego-motion estimation. A 3D relative-motion model that describes spatial relations among objects is exploited for predicting object states robustly and recovering objects when occlusion and mis-detections occur. Reversible jump Markov chain Monte Carlo (RJMCMC) particle filtering is applied to solve the posteriori estimation problem. Both quantitative and qualitative experiments with benchmark datasets and video collected on campus were conducted, which confirms that the proposed method is outperformed in many evaluation metrics.

Efficient Frame Error Concealment Using Bilateral Motion Estimation for Low Bit-Rate Video Transmission

2009 ◽  
Vol E92-B (2) ◽  
pp. 461-472
Author(s):  
DinhTrieu DUONG ◽  
Min-Cheol HWANG ◽  
Byeong-Doo CHOI ◽  
Jun-Hyung KIM ◽  
Sung-Jea KO
Keyword(s):  
Motion Estimation ◽  
Video Transmission ◽  
Error Concealment ◽  
Bit Rate ◽  
Low Bit Rate

Low-Power Motion Estimation Processor with 3D Stacked Memory

2015 ◽  
Vol E98.A (7) ◽  
pp. 1431-1441 ◽  
Author(s):  
Shuping ZHANG ◽  
Jinjia ZHOU ◽  
Dajiang ZHOU ◽  
Shinji KIMURA ◽  
Satoshi GOTO
Keyword(s):  
Motion Estimation ◽  
Low Power ◽  
Stacked Memory
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