Mapping of Rescue Environment Based on NDT Scan Matching

2013 ◽  
Vol 760-762 ◽  
pp. 928-933 ◽  
Author(s):  
Jin Liang Li ◽  
You Xia Sun
Keyword(s):  
Feature Extraction ◽  
Probability Distribution ◽  
Kalman Filtering ◽  
Matching Method ◽  
Scan Matching ◽  
Mapping Problem ◽  
Rescue Robots ◽  
Scan Data ◽  
Slam Algorithm ◽  
Environment Experiment

This paper studied the mapping problem for rescue robots based on laser scan matching and extend Kalman filtering (EKF). Because of the non-structural rescue environments, it is hard to extract typical features. Scan matching method based on normal distribution transform (NDT) can avoid the hard feature extraction problem by estimation of the probability distribution of laser scan data. By fusing NDT scan matching with EKF framework, the NDT-EKF SLAM algorithm was proposed, which can effectively and precisely build maps for rescue environment. Experiment results show that NDT-EKF SLAM algorithm is more precise than algorithms based solely on scan-matching.

Localization for a Rescue Robot Based on NDT Scan Matching

2010 ◽  
Vol 439-440 ◽  
pp. 445-450 ◽  
Author(s):  
Jin Liang Li ◽  
Ji Hua Bao ◽  
Yan Yu
Keyword(s):  
Feature Extraction ◽  
Probability Distribution ◽  
Kalman Filtering ◽  
Precise Localization ◽  
Localization Problem ◽  
Extended Kalman Filtering ◽  
Matching Method ◽  
Scan Matching ◽  
Rescue Robot ◽  
Scan Data

This paper studied the localization problem for a rescue robot based on laser scan matching and extended Kalman filtering (EKF). Scan matching method based on normal distribution transform (NDT) can avoid hard feature extraction problem by estimation of the probability distribution of laser scan data and localization can be achieved using correlation of the NDT. Based on NDT scan matching, the NDT-EKF algorithm is proposed , which realizes fast and precise localization in rescue environment by fusing odometery data and scan matching together. The NDT-EKF algorithm has been extensively tested and experimental results show its effectiveness and robustness.

Dynamic Obstacle Detection Using Visually Informed Scan Matching

2013 ◽  
Vol 313-314 ◽  
pp. 1192-1196
Author(s):  
Tae Seok Lee ◽  
Heon Cheol Lee ◽  
Won Sok Yoo ◽  
Doo Jin Kim ◽  
Beom Hee Lee
Keyword(s):  
Laser Scanner ◽  
Dynamic Environments ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Matching Method ◽  
Scan Matching ◽  
Time Dynamic ◽  
Dynamic Obstacle ◽  
Scan Data ◽  
Robot Position

This paper presents a real-time dynamic obstacle detection algorithm using a scan matching method considering image information from a mobile robot equipped with a camera and a laser scanner. By combining image and laser scan data, we extract a scan segment corresponding to the dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments, the extracted scan segment is temporarily removed. After obtaining a good robot position, the position of the dynamic obstacle is calculated based on the robot’s position. Through two experimental scenarios, the performance of the proposed algorithm is demonstrated.

Audio feature extraction using probability distribution function

2015 ◽  
Author(s):  
Suhaib A. ◽  
Khairunizam Wan ◽  
Azri A. Aziz ◽  
D. Hazry ◽  
Zuradzman M. Razlan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Audio Feature ◽  
Audio Feature Extraction

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

A novel prediction model of traffic accidents based on big data

2019 ◽  
Vol 10 (04) ◽  
pp. 1950022 ◽  
Author(s):  
Minglei Song ◽  
Rongrong Li ◽  
Binghua Wu
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Probability Distribution ◽  
Prediction Model ◽  
Probability Density ◽  
Traffic Accidents ◽  
Joint Probability ◽  
Distributed Database ◽  
Joint Probability Distribution ◽  
Joint Probability Density

The occurrence of traffic accidents is regular in probability distribution. Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance. In recent years, prediction methods of traffic accidents used by researchers have some problems, such as low calculation accuracy. Therefore, a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper. First, a function of big data joint probability distribution for traffic accidents is established. Second, establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents, and then extracting the joint probability density feature of big data for traffic accident probability distribution. According to the result of feature extraction, adaptive functional and directivity are predicted, and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering, so as to optimize the design of the prediction model of traffic accidents based on big data. Simulation results show that in predicting traffic accidents, the model in this paper has advantages of relatively high accuracy, relatively good confidence and stable prediction result.

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