A Comparison of Low-Cost Monocular Vision Techniques for Pothole Distance Estimation

2015 ◽  
Author(s):  
S. Nienaber ◽  
R.S. Kroon ◽  
M.J. Booysen
Keyword(s):  
Low Cost ◽  
Distance Estimation ◽  
Monocular Vision

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

2018 ◽  
Vol 06 (04) ◽  
pp. 267-275
Author(s):  
Ajay Shankar ◽  
Mayank Vatsa ◽  
P. B. Sujit
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Support Vector ◽  
Svm Classifier ◽  
Range Finding ◽  
Image Patches ◽  
Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

Inter-Vehicle Distance Estimation Method Based on Monocular Vision Using 3D Detection

2020 ◽  
Vol 69 (5) ◽  
pp. 4907-4919 ◽  
Author(s):  
Ting Zhe ◽  
Liqin Huang ◽  
Qiang Wu ◽  
Jianjia Zhang ◽  
Chenhao Pei ◽  
...  
Keyword(s):  
Estimation Method ◽  
Distance Estimation ◽  
Monocular Vision

scholarly journals Sliding mode collision-free navigation for quadrotors using monocular vision

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1493-1509
Author(s):  
Diego Mercado ◽  
Pedro Castillo ◽  
Rogelio Lozano
Keyword(s):  
Sliding Mode ◽  
Low Cost ◽  
Depth Map ◽  
Monocular Vision ◽  
Scale Separation ◽  
External Perturbations ◽  
Time Scale Separation ◽  
Mode 2 ◽  
Inertial Measurements ◽  
Vision Algorithm

SUMMARYSafe and accurate navigation for autonomous trajectory tracking of quadrotors using monocular vision is addressed in this paper. A second order Sliding Mode (2-SM) control algorithm is used to track desired trajectories, providing robustness against model uncertainties and external perturbations. The time-scale separation of the translational and rotational dynamics allows to design position controllers by giving a desired reference in roll and pitch angles, which is suitable for practical validation in quad-rotors equipped with an internal attitude controller. A Lyapunov based analysis proved the closed-loop stability of the system despite the presence of unknown external perturbations. Monocular vision fused with inertial measurements are used to estimate the vehicle's pose with respect to unstructured scenes. In addition, the distance to potential collisions is detected and computed using the sparse depth map coming also from the vision algorithm. The proposed strategy is successfully tested in real-time experiments, using a low-cost commercial quadrotor.

scholarly journals A Low-Cost On-Street Parking Management System Based on Bluetooth Beacons

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4559 ◽  
Author(s):  
Chi-Fang Chien ◽  
Hui-Tzu Chen ◽  
Chi-Yi Lin
Keyword(s):  
Management System ◽  
Low Cost ◽  
Distance Estimation ◽  
Magnetic Sensors ◽  
License Plate ◽  
City Governments ◽  
Human Labor ◽  
Parking Management ◽  
Information And Communication ◽  
Smart Parking

In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. However, using wireless magnetic sensors can only detect the occupancy of parking spaces without the knowledge of who are currently using these parking spaces; human labor is still needed to issue the parking bills. In contrast, smart parking meters based on image recognition can detect the occupancy of parking spaces along with the license plate numbers, but the cost of deploying smart parking meters is relatively high. In this research, we investigate the feasibility of building an on-street parking management system mainly based on low-cost Bluetooth beacons. Specifically, beacon transmitters are installed in the vehicles, and beacon receivers are deployed along the roadside parking spaces. By processing the received beacon signals using Kalman filter, our system can detect the occupancy of parking spaces as well as the identification of the vehicles. Although distance estimation using the received signal strength is not accurate, our experiments show that it suffices for correct detection of parking occupancy.

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