scholarly journals Enhanced Localization with Adaptive Normal Distribution Transform Monte Carlo Localization for Map Based Navigation Robot

2019 ◽  
Vol 18 (3-2) ◽  
pp. 17-24 ◽  
Author(s):  
T.Y. Lim ◽  
C. F. Yeong ◽  
E. L. M. Su ◽  
S.M. Shithil ◽  
S.F. Chik ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Normal Distribution ◽  
High Speed ◽  
Research Work ◽  
Computational Effort ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Leibler Divergence ◽  
Monte Carlo Localization ◽  
Number Of Particles

Map-based navigation is the common navigation method used among the mobile robotic application. The localization plays an important role in the navigation where it estimates the robot position in an environment. Monte Carlo Localization (MCL) is found as the widely used estimation algorithm due to it non-linear characteristic. There are classifications of MCL such as Adaptive MCL (AMCL), Normal Distribution Transform MCL (NDT-MCL) which can perform better than the MCL. However, AMCL is adaptive to particles but the position estimation accuracy is not optimized. NDT-MCL has good position estimation but it requires higher number of particles which results in higher computational effort. The objective of the research is to design and develop a localization algorithm which can achieve better performance in term of position estimation and computational effort. The new MCL algorithm which is named as Adaptive Normal Distribution Transform Monte Carlo Localization (ANDT-MCL) is then designed and developed. It integrates Kullback–Leibler divergence, Normal Distribution Transform and Systematic Resampling into the algorithm. Three experiments are conducted to evaluate the performance of proposed ANDT-MCL in simulated environment. These experiments include evaluating the performance of ANDT-MCL with different path shape, distance and velocity. In the end of the research work, the proposed ANDT-MCL is successfully developed. It is adaptive to the number of particles used, higher position estimation and lower computational effort than existing algorithms. The algorithm can produce better position estimation with less computational effort in any kind paths and is consistent in long journey as well as can outperform in high speed navigation.

scholarly journals ROS-based localization of a race vehicle at high-speed using LIDAR

2019 ◽  
Vol 95 ◽  
pp. 04002 ◽  
Author(s):  
Tim Stahl ◽  
Alexander Wischnewski ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo ◽  
Performance Evaluation ◽  
Kalman Filter ◽  
Pose Estimation ◽  
High Speed ◽  
Position Estimation ◽  
Redundant Information ◽  
Adaptive Monte Carlo ◽  
High Speeds ◽  
Monte Carlo Localization

An approach for LIDAR-based localization at high speeds is presented. In the proposed framework, the laser pose estimation is treated as a parallel redundant information, which is fused in an adjacent Kalman filter. The measurement and motion update step of the ROS-based adaptive Monte Carlo localization package is modified, in order to meet the requirements of a high-speed race scenario. Thereby, the key focus is on computational efficiency and the adaptation to characteristics arising at high speeds and at the limits of handling. An introspective performance evaluation monitors the position estimation process and labels generated outputs for adjacent components accordingly. The effectiveness of the proposed algorithm is illustrated in a real world high-speed experiment, autonomously driving a race vehicle – the DevBot – in a typical race environment.

scholarly journals Experimental Comparison between Event and Global Shutter Cameras

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1137
Author(s):  
Ondřej Holešovský ◽  
Radoslav Škoviera ◽  
Václav Hlaváč ◽  
Roman Vítek
Keyword(s):  
High Speed ◽  
Ground Truth ◽  
Motion Blur ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Experimental Comparison ◽  
Estimation Errors ◽  
Detection Rates ◽  
Ballistic Experiment ◽  
Event Camera

We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

scholarly journals TI-ADC multi-channel mismatch estimation and calibration in ultra-high-speed optical signal acquisition system

2021 ◽  
Vol 18 (6) ◽  
pp. 9050-9075
Author(s):  
Yongjie Zhao ◽  
◽  
Sida Li ◽  
Zhiping Huang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
High Speed ◽  
Dynamic Range ◽  
Optical Signal ◽  
Estimation Accuracy ◽  
Signal Acquisition ◽  
Analog To Digital ◽  
Channel Mismatch ◽  
Background Mode

<abstract> <p>This article presents a method to calibrate a 16-channel 40 GS/s time-interleaved analog-to-digital converter (TI-ADC) based on channel equalization and Monte Carlo method. First, the channel mismatch is estimated by the Monte Carlo method, and equalize each channel to meet the calibration requirement. This method does not require additional hardware circuits, every channel can be compensated. The calibration structure is simple and the convergence speed is fast, besides, the ADC is worked in background mode, which does not affect the conversion. The prototype, implemented in 28 nm CMOS, reaches a 41 dB SFDR with an input signal of 1.2 GHz and 5 dBm after the proposed background offset and gain mismatch calibration. Compared with previous works, the spurious-free dynamic range (SFDR) and the effective number of bits (ENOB) are better, the estimation accuracy is higher, the error is smaller and the faster speed of convergence improves the efficiency of signal processing.</p> </abstract>

Cloud-based improved Monte Carlo localization algorithm with robust orientation estimation for mobile robots

2018 ◽  
Vol 36 (1) ◽  
pp. 178-203 ◽  
Author(s):  
I-hsum Li ◽  
Wei-Yen Wang ◽  
Chung-Ying Li ◽  
Jia-Zwei Kao ◽  
Chen-Chien Hsu
Keyword(s):  
Monte Carlo ◽  
Pose Estimation ◽  
Indoor Localization ◽  
Estimation Accuracy ◽  
Localization Algorithm ◽  
Orientation Estimation ◽  
Content Type ◽  
Innovative Strategies ◽  
Weight Calculation ◽  
Monte Carlo Localization

Purpose This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization. Design/methodology/approach The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability. Findings For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system. Originality/value The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.

A new direct simulation Monte Carlo implementation for more efficient simulation of hypersonic flow over arbitrarily shaped bodies using dynamic cells

2016 ◽  
Vol 231 (1) ◽  
pp. 82-97
Author(s):  
RV Reji ◽  
S Anil Lal
Keyword(s):  
Monte Carlo ◽  
High Speed ◽  
Direct Simulation Monte Carlo ◽  
Computational Time ◽  
Optimum Number ◽  
Gas Flows ◽  
Direct Simulation ◽  
Simulation Monte Carlo ◽  
Boundary Curves ◽  
Number Of Particles

Methods are reported for less computationally expensive and more accurate implementations of the direct simulation Monte Carlo (DSMC) method for the simulation of high speed gas flows over arbitrarily shaped bodies. A new particle-tracking algorithm with a saving of computational time of up to 10% is reported in which tracking of particles is done with the help of big triangles having vertices lying on the boundary curves. An algorithm has been developed to generate DSMC cells for collision and sampling that contain a prescribed number of molecules. This algorithm is able to generate over 90% cells having the optimum number of seven or eight molecules for simulating collisions. Sampling for macroscopic properties is done on dynamic cells that contain a number of particles varying spatially as a function of the local number density. A criterion for finding the number of particles in sampling cells is presented. This criterion has been found to result in accurate and fast simulation of two-dimensional hypersonic flows of argon over a wedge, and argon and nitrogen over a circular cylinder.

scholarly journals IMPROVEMENT OF 3D MONTE CARLO LOCALIZATION USING A DEPTH CAMERA AND TERRESTRIAL LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 61-66 ◽  
Author(s):  
S. Kanai ◽  
R. Hatakeyama ◽  
H. Date
Keyword(s):  
Monte Carlo ◽  
Motion Estimation ◽  
Indoor Localization ◽  
Laser Scanner ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Inertia Sensor ◽  
Number Of Particles

Effective and accurate localization method in three-dimensional indoor environments is a key requirement for indoor navigation and lifelong robotic assistance. So far, Monte Carlo Localization (MCL) has given one of the promising solutions for the indoor localization methods. Previous work of MCL has been mostly limited to 2D motion estimation in a planar map, and a few 3D MCL approaches have been recently proposed. However, their localization accuracy and efficiency still remain at an unsatisfactory level (a few hundreds millimetre error at up to a few FPS) or is not fully verified with the precise ground truth. Therefore, the purpose of this study is to improve an accuracy and efficiency of 6DOF motion estimation in 3D MCL for indoor localization. Firstly, a terrestrial laser scanner is used for creating a precise 3D mesh model as an environment map, and a professional-level depth camera is installed as an outer sensor. GPU scene simulation is also introduced to upgrade the speed of prediction phase in MCL. Moreover, for further improvement, GPGPU programming is implemented to realize further speed up of the likelihood estimation phase, and anisotropic particle propagation is introduced into MCL based on the observations from an inertia sensor. Improvements in the localization accuracy and efficiency are verified by the comparison with a previous MCL method. As a result, it was confirmed that GPGPU-based algorithm was effective in increasing the computational efficiency to 10-50 FPS when the number of particles remain below a few hundreds. On the other hand, inertia sensor-based algorithm reduced the localization error to a median of 47mm even with less number of particles. The results showed that our proposed 3D MCL method outperforms the previous one in accuracy and efficiency.

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

Investigation of Neutral Particles Using High Speed Camera and Monte-Carlo Simulation in the GAMMA 10 Central-Cell

2007 ◽  
Vol 51 (2T) ◽  
pp. 82-85 ◽  
Author(s):  
Y. Nakashima ◽  
Y. Higashizono ◽  
N. Nishino ◽  
H. Kawano ◽  
M.K. Islam ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Speed ◽  
Central Cell ◽  
High Speed Camera ◽  
Neutral Particles

scholarly journals Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Xu ◽  
Yiwen Wang ◽  
Fang Wang ◽  
Yuxi Liao ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Neuronal Response ◽  
Position Estimation ◽  
Monte Carlo Algorithm ◽  
Sequential Monte Carlo Method ◽  
Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

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