Pose Estimation Based on Monocular Visual Odometry and Lane Detection for Intelligent Vehicles

2018 ◽  
pp. 562-566 ◽  
Author(s):  
Juan Galarza ◽  
Esteban Pérez ◽  
Esteban Serrano ◽  
Andrés Tapia ◽  
Wilbert G. Aguilar
Keyword(s):  
Pose Estimation ◽  
Visual Odometry ◽  
Intelligent Vehicles ◽  
Lane Detection

Improved pose estimation by inlier refinement for visual odometry

2017 ◽  
Author(s):  
Rohan More ◽  
Rahul Kottath ◽  
R. Jegadeeshwaran ◽  
Vipan Kumar ◽  
Vinod Karar ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Visual Odometry

Swift Path Planning: Vehicle Localization by Visual Odometry Trajectory Tracking and Mapping

2018 ◽  
Vol 06 (04) ◽  
pp. 221-230
Author(s):  
Dayang Nur Salmi Dharmiza Awang Salleh ◽  
Emmanuel Seignez
Keyword(s):  
Path Planning ◽  
Low Cost ◽  
Rapid Development ◽  
Random Noise ◽  
Visual Odometry ◽  
Path Selection ◽  
Intelligent Vehicles ◽  
Signal Loss ◽  
Visual Sensor ◽  
Vehicle Localization

Accurate localization is the key component in intelligent vehicles for navigation. With the rapid development especially in urban area, the increasing high-rise buildings results in urban canyon and road network has become more complex. These affect the vehicle navigation performance particularly in the event of poor Global Positioning System (GPS) signal. Therefore, it is essential to develop a perceptive localization system to overcome this problem. This paper proposes a localization approach that exhibits the advantages of Visual Odometry (VO) in low-cost data fusion to reduce vehicle localization error and improve its response rate in path selection. The data used are sourced from camera as visual sensor, low-cost GPS and free digital map from OpenStreetMap. These data are fused by Particle filter (PF) where our method estimates the curvature similarity score of VO trajectory curve with candidate ways extracted from the map. We evaluate the robustness of our proposed approach with three types of GPS errors such as random noise, biased noise and GPS signal loss in an instance of ambiguous road decision. Our results show that this method is able to detect and select the correct path simultaneously which contributes to a swift path planning.

scholarly journals Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3166 ◽  
Author(s):  
Cao ◽  
Song ◽  
Song ◽  
Xiao ◽  
Peng
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Processing Time ◽  
Dynamic Environments ◽  
Detection Algorithm ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
Detection Accuracy ◽  
Traditional Methods ◽  
Time Performance

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

scholarly journals Solving Monocular Visual Odometry Scale Factor with Adaptive Step Length Estimates for Pedestrians Using Handheld Devices

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 953 ◽  
Author(s):  
Nicolas Antigny ◽  
Hideaki Uchiyama ◽  
Myriam Servières ◽  
Valérie Renaudin ◽  
Diego Thomas ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Geographical Information Systems ◽  
Digital Terrain Model ◽  
Visual Odometry ◽  
Step Length ◽  
Geographical Information ◽  
Handheld Devices ◽  
Measurement Unit ◽  
Handheld Device ◽  
Adaptive Step

The urban environments represent challenging areas for handheld device pose estimation (i.e., 3D position and 3D orientation) in large displacements. It is even more challenging with low-cost sensors and computational resources that are available in pedestrian mobile devices (i.e., monocular camera and Inertial Measurement Unit). To address these challenges, we propose a continuous pose estimation based on monocular Visual Odometry. To solve the scale ambiguity and suppress the scale drift, an adaptive pedestrian step lengths estimation is used for the displacements on the horizontal plane. To complete the estimation, a handheld equipment height model, with respect to the Digital Terrain Model contained in Geographical Information Systems, is used for the displacement on the vertical axis. In addition, an accurate pose estimation based on the recognition of known objects is punctually used to correct the pose estimate and reset the monocular Visual Odometry. To validate the benefit of our framework, experimental data have been collected on a 0.7 km pedestrian path in an urban environment for various people. Thus, the proposed solution allows to achieve a positioning error of 1.6–7.5% of the walked distance, and confirms the benefit of the use of an adaptive step length compared to the use of a fixed-step length.

scholarly journals Development of Stereo Visual Odometry Based on Photogrammetric Feature Optimization

2019 ◽  
Vol 11 (1) ◽  
pp. 67 ◽  
Author(s):  
Sung-Joo Yoon ◽  
Taejung Kim
Keyword(s):  
Pose Estimation ◽  
Automobile Industry ◽  
Visual Odometry ◽  
Geometric Interpretation ◽  
Bundle Adjustment ◽  
Processing Technologies ◽  
Extraction Step ◽  
Feature Optimization ◽  
Institute Of Technology ◽  
Stereo Visual Odometry

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.

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