Stereo visual odometry with velocity constraint for ground vehicle applications

2021 ◽  
pp. 1-13
Author(s):  
Fei Liu ◽  
Yashar Balazadegan Sarvrood ◽  
Yue Liu ◽  
Yang Gao

Abstract This paper proposes a novel method of error mitigation for stereo visual odometry (VO) applied in land vehicles. A non-holonomic constraint (NHC), which imposes physical constraint to the rightward velocity of a land vehicle, is implemented as an observation in an extended Kalman filter (EKF) to reduce the drift of stereo VO. The EKF state vector includes position errors in an Earth-centred, Earth-fixed (ECEF) frame, velocity errors in the camera frame, angular rate errors and attitude errors. All the related equations are described and presented in detail. In this approach, no additional sensors are used but NHC, namely velocity constraint in the right direction , is applied as an external measurement to improve the accuracy. Tests are conducted with the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets. Results show that the relative horizontal positioning error improved from 0⋅63% to 0⋅22% on average with the application of the velocity constraints. The maximum and root mean square of the horizontal error with velocity constraints are both reduced to less than half of the error with stand-alone stereo VO.

2019 ◽  
Vol 11 (1) ◽  
pp. 67 ◽  
Author(s):  
Sung-Joo Yoon ◽  
Taejung Kim

One of the important image processing technologies is visual odometry (VO) technology. VO estimates platform motion through a sequence of images. VO is of interest in the virtual reality (VR) industry as well as the automobile industry because the construction cost is low. In this study, we developed stereo visual odometry (SVO) based on photogrammetric geometric interpretation. The proposed method performed feature optimization and pose estimation through photogrammetric bundle adjustment. After corresponding the point extraction step, the feature optimization was carried out with photogrammetry-based and vision-based optimization. Then, absolute orientation was performed for pose estimation through bundle adjustment. We used ten sequences provided by the Karlsruhe institute of technology and Toyota technological institute (KITTI) community. Through a two-step optimization process, we confirmed that the outliers, which were not removed by conventional outlier filters, were removed. We also were able to confirm the applicability of photogrammetric techniques to stereo visual odometry technology.


2021 ◽  
pp. 1-18
Author(s):  
Yi Zhou ◽  
Guillermo Gallego ◽  
Shaojie Shen

Author(s):  
Sara Farboud-Sheshdeh ◽  
Timothy D. Barfoot ◽  
Raymond H. Kwong

Sensor Review ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 157-167 ◽  
Author(s):  
Shengbo Sang ◽  
Ruiyong Zhai ◽  
Wendong Zhang ◽  
Qirui Sun ◽  
Zhaoying Zhou

Purpose – This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy. Design/methodology/approach – The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases. Findings – The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion. Originality/value – The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.


Author(s):  
Arturo de la Escalera ◽  
Ebroul Izquierdo ◽  
David Martín ◽  
Fernando García ◽  
José María Armingol

Author(s):  
Chuanye Tang ◽  
Xinwen Zhao ◽  
Jianfeng Chen ◽  
Long Chen ◽  
Yazhou Zhou

Author(s):  
BOUCHELOUKH Abdelghani ◽  
BOUDJEMA Fares ◽  
NEMRA Abdelkrim ◽  
DEMIM Fethi ◽  
LOUALI Rabah

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