scholarly journals Outdoor SLAM Using Monocular Vision-Based Localization with LIDAR-Aided Mapping for service robot in Highway

CONVERTER ◽  
2021 ◽  
pp. 397-406
Author(s):  
Shuwen Pan, Yuanyuan Li, Pengying Du, Yan Liu
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Monocular Vision ◽  
Service Robot ◽  
Robot System ◽  
Occupancy Grid ◽  
Occupancy Grid Map ◽  
Vision Based Localization ◽  
Slam Algorithm ◽  
Environmental Map

This paper designed an intelligent service robot system in highway based on multi-sensor fusion. The mobile robot attempts to fuse the lidar information and monocular vision information to estimate the pose of itself and obtain an environmental map. It adapts a new SLAM method which combines lidar and vision information. Lidar is used to obtain the 2D occupancy grid map and the monocular vision SLAM algorithm uses the Extended Kalman Filter (EKF) to magnify the pose estimation. The 3-DOF pose provided by lidar is obtained through Cartographer algorithm and the monocular vision SLAM who offers the 6-DOF pose is realized with ORB-SLAM. The experimental results show that the system is effective in application as an intelligent service robot of highway.

scholarly journals An Approach for 2D Visual Occupancy Grid Map Using Monocular Vision

2011 ◽  
Vol 281 ◽  
pp. 175-191 ◽  
Author(s):  
André M. Santana ◽  
Kelson R.T. Aires ◽  
Rodrigo M.S. Veras ◽  
Adelardo A.D. Medeiros
Keyword(s):  
Monocular Vision ◽  
Occupancy Grid ◽  
Grid Map ◽  
Occupancy Grid Map

Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

Communicating Vehicles in Convoy and Monocular Vision-based Localization

2010 ◽  
Vol 43 (16) ◽  
pp. 73-78 ◽  
Author(s):  
Thomas Féraud ◽  
Paul Checchin ◽  
Romuald Aufrère ◽  
Roland Chapuis
Keyword(s):  
Monocular Vision ◽  
Vision Based Localization

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.

Training‐based head pose estimation under monocular vision

2016 ◽  
Vol 10 (8) ◽  
pp. 798-805 ◽  
Author(s):  
Zhizhi Guo ◽  
Qianxiang Zhou ◽  
Zhongqi Liu ◽  
Chunhui Liu
Keyword(s):  
Pose Estimation ◽  
Monocular Vision ◽  
Head Pose Estimation ◽  
Head Pose

scholarly journals Application of the extended Kalman filter to Lidar pose estimation

2021 ◽  
Author(s):  
Marcin Kuryllo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation

Application of the extended Kalman filter to Lidar pose estimation

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