Improved intelligent vehicle self-localization with integration of sparse visual map and high-speed pavement visual odometry

2020 ◽  
Vol 235 (1) ◽  
pp. 177-187
Author(s):  
Gang Huang ◽  
Zhaozheng Hu ◽  
Qianwen Tao ◽  
Fan Zhang ◽  
Zhe Zhou
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Visual Odometry ◽  
Lateral View ◽  
Intelligent Vehicles ◽  
High Speed Camera ◽  
Vehicle Localization ◽  
Localization And Mapping ◽  
Resolution Imaging ◽  
Fundamental Requirement

Localization is a fundamental requirement for intelligent vehicles. Conventional localization methods usually suffer from various limitations, such as low accuracy and blocked areas for Global Positioning System, high cost for inertial navigation system or light detection and ranging, and low robustness for visual simultaneous localization and mapping or visual odometry. To overcome these problems, we propose a novel localization method integrated with a sparse visual map and a high-speed pavement visual odometry. We use a lateral-view camera to sense the sparse visual map node for accurate map-based localization. We use a down-view high-speed camera for odometry computation between two sparse visual map nodes. With a high-speed camera, it is possible to extract and track pavement features with stable resolution imaging even in high-speed movement. We also develop a data-driven motion model for the Kalman filter to fuse the localization results from the sparse map and the high-speed pavement visual odometry to enhance vehicle localization. The proposed method was tested in two different scenarios in different pavement conditions. The experimental results demonstrate that the proposed method can improve vehicle localization with low cost and high feasibility.

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 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.

scholarly journals Development of a low-cost, user-customizable, high-speed camera

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0232788
Author(s):  
Yamn Chalich ◽  
Avijit Mallick ◽  
Bhagwati Gupta ◽  
M. Jamal Deen
Keyword(s):  
High Speed ◽  
Low Cost ◽  
High Speed Camera

scholarly journals A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Zhan Wang ◽  
Alain Lambert
Keyword(s):  
Constraint Satisfaction Problem ◽  
Low Cost ◽  
Dead Reckoning ◽  
Constraint Propagation ◽  
Vehicle Localization ◽  
Practical Applications ◽  
Localization And Mapping ◽  
Position And Orientation ◽  
Map Data ◽  
Interval Constraint

Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

The Concurrent Validity and Reliability of a Low-Cost, High-Speed Camera-Based Method for Measuring the Flight Time of Vertical Jumps

2014 ◽  
Vol 28 (2) ◽  
pp. 528-533 ◽  
Author(s):  
Carlos Balsalobre-Fernández ◽  
Carlos M. Tejero-González ◽  
Juan del Campo-Vecino ◽  
Nicolás Bavaresco
Keyword(s):  
Concurrent Validity ◽  
High Speed ◽  
Low Cost ◽  
Flight Time ◽  
High Speed Camera ◽  
Validity And Reliability ◽  
Vertical Jumps

scholarly journals FloorVLoc: A Modular Approach to Floorplan Monocular Localization

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 69
Author(s):  
John Noonan ◽  
Ehud Rivlin ◽  
Hector Rotstein
Keyword(s):  
Linear Optimization ◽  
Intelligent Vehicles ◽  
Average Error ◽  
Vehicle Localization ◽  
Positioning Systems ◽  
Monocular Camera ◽  
Accurate Localization ◽  
Emergency Personnel ◽  
Localization And Mapping ◽  
Global Positioning

Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.

scholarly journals Comparisons of flow dynamics of dual-blade to single-blade beveled-tip vitreous cutters

2021 ◽  
Author(s):  
Makoto Inoue ◽  
Takashi Koto ◽  
Akito Hirakata
Keyword(s):  
Salt Solution ◽  
High Speed ◽  
Lateral View ◽  
Flow Dynamics ◽  
High Speed Camera ◽  
Mean Flow ◽  
Cutting Rate ◽  
The Mean ◽  
Fluorescent Polymer ◽  
End Results

Introduction: To compare the flow dynamics of the dual-blade to the single-blade beveled-tip vitreous cutters. Methods: The aspiration rates of balanced salt solution (BSS) and swine vitreous were measured for the 25-gauge and 27-gauge dual- and single-blade vitreous cutters. The flow dynamics of BSS and diluted vitreous mixed with fluorescent polymer at the maximal cutting rates and the reflux of BSS were measured in images obtained by a high-speed camera. The distal end of the cutter was defined as the head end. Results: The aspiration rates of BSS and vitreous by the 25- and 27-gauge dual-blade cutters were significantly higher than those of both single-blade cutters at the maximal cutting rate (all P≤0.01). The mean aspiration flow of BSS in front of the port from a lateral view was significantly faster for both dual-blade cutters than for both single-blade cutters (P=0.003, P=0.019). The angle of the mean flow of BSS of both dual-blade cutters was from the distal end (P<0.001, P<0.001) but that of the single blade-cutters was from the proximal end. The velocity and angle of the mean reflux flow of both types of cutters were not significantly different. The mean aspiration flow of diluted vitreous was significantly faster for 25-gauge dual-blade cutters with the angle more from the proximal end and 27-gauge dual-blade cutters more from the distal end than both single-blade cutters (P=0.018, P=0.048). Conclusion: The dual-blade beveled-tip vitreous cutters improve the efficiency of the vitrectomy procedures and maintain the distal aspirating flow by the beveled-tip.

Very-low-cost high-speed camera controller and camera simulator for RTLinux for the CHARA array

2003 ◽  
Author(s):  
Braden E. Hines ◽  
Theo A. ten Brummelaar ◽  
Mark A. Shure
Keyword(s):  
High Speed ◽  
Low Cost ◽  
High Speed Camera ◽  
Chara Array

Detecting Vibration Problems in Machines and Structures Using Motion Capturing by Camera

2021 ◽  
Vol 21 (1) ◽  
pp. 38-49
Author(s):  
Husam Sattar Jasim ◽  
Jaafar Khalaf Ali
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Vibration Signal ◽  
Vibration Measurement ◽  
High Speed Camera ◽  
Full Field ◽  
Phase Angle Difference ◽  
Vibration Data ◽  
Vibration Sensors ◽  
The Time Domain

Vibration in rotating machines and structures is normally measured using accelerometers and other vibration sensors. For large machines and structures, the process of collecting vibration data is tedious and time-consuming due to the large number of points where vibration data must be measured. In this paper, a novel non-contact vibration measurement method has been introduced by using a high-speed camera as a vibration measurement device. This method has many advantages compared with the others. It has a low cost, easy to setup, and high automation. It also can be used for full-field measurement. Many tests have been accomplished to prove the validation of this method. The verification test has been accomplished by using the machinery faults simulator. It presented a reasonable validation that the operation deflection shapes (ODS) and the phase difference of any object can be successfully measured by using a high-speed camera. The mode shape tests have been accomplished by using the whirling of shaft apparatus device to extract the time domain, frequency domain, ODS, and phase differences for many points on the shaft at the first two critical speeds. The results proved that the high-speed camera can be used to detect the vibration signal in many different fault cases. It also proved that the high-speed camera can be used to detect the ODS and the phase angle difference. That gives the proposed method more robust and acceptance.

Sign in / Sign up

Export Citation Format

Share Document