scholarly journals Robust Monocular Visual Odometry Trajectory Estimation in Urban Environments

2019 ◽  
Vol 11 (10) ◽  
pp. 12-18
Author(s):  
Ahmed Abdu ◽  
◽  
Hakim A. Abdo ◽  
Al-Alimi Dalal
Keyword(s):  
Visual Odometry ◽  
Urban Environments ◽  
Trajectory Estimation

scholarly journals Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 939 ◽  
Author(s):  
Safa Ouerghi ◽  
Rémi Boutteau ◽  
Xavier Savatier ◽  
Fethi Tlili
Keyword(s):  
Visual Odometry ◽  
Urban Environments ◽  
Position Tracking ◽  
Place Recognition ◽  
Vehicle Position

Stereo camera visual odometry for moving urban environments

2019 ◽  
Vol 26 (3) ◽  
pp. 243-256 ◽  
Author(s):  
Patrice Delmas ◽  
Trevor Gee
Keyword(s):  
Visual Odometry ◽  
Urban Environments ◽  
Stereo Camera

scholarly journals Forward and Backward Visual Fusion Approach to Motion Estimation with High Robustness and Low Cost

2019 ◽  
Vol 11 (18) ◽  
pp. 2139
Author(s):  
Ke Wang ◽  
Xin Huang ◽  
JunLan Chen ◽  
Chuan Cao ◽  
Zhoubing Xiong ◽  
...  
Keyword(s):  
Low Cost ◽  
Visual Odometry ◽  
Optimization Method ◽  
Measurement Unit ◽  
Ground Vehicles ◽  
Unit Quaternion ◽  
Trajectory Estimation ◽  
Evaluation Indexes ◽  
Error Space ◽  
Self Motion

We present a novel low-cost visual odometry method of estimating the ego-motion (self-motion) for ground vehicles by detecting the changes that motion induces on the images. Different from traditional localization methods that use differential global positioning system (GPS), precise inertial measurement unit (IMU) or 3D Lidar, the proposed method only leverage data from inexpensive visual sensors of forward and backward onboard cameras. Starting with the spatial-temporal synchronization, the scale factor of backward monocular visual odometry was estimated based on the MSE optimization method in a sliding window. Then, in trajectory estimation, an improved two-layers Kalman filter was proposed including orientation fusion and position fusion . Where, in the orientation fusion step, we utilized the trajectory error space represented by unit quaternion as the state of the filter. The resulting system enables high-accuracy, low-cost ego-pose estimation, along with providing robustness capability of handing camera module degradation by automatic reduce the confidence of failed sensor in the fusion pipeline. Therefore, it can operate in the presence of complex and highly dynamic motion such as enter-in-and-out tunnel entrance, texture-less, illumination change environments, bumpy road and even one of the cameras fails. The experiments carried out in this paper have proved that our algorithm can achieve the best performance on evaluation indexes of average in distance (AED), average in X direction (AEX), average in Y direction (AEY), and root mean square error (RMSE) compared to other state-of-the-art algorithms, which indicates that the output results of our approach is superior to other methods.

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