scholarly journals FastSLAM Using Compressed Occupancy Grids

2016 ◽  
Vol 2016 ◽  
pp. 1-23 ◽  
Author(s):  
Christopher Cain ◽  
Alexander Leonessa
Keyword(s):  
Particle Filter ◽  
Visual Odometry ◽  
Experimental Results ◽  
Ground Vehicle ◽  
Occupancy Grid ◽  
Unknown Environments ◽  
Occupancy Grids ◽  
Robotic Vehicles ◽  
Full Occupancy

Robotic vehicles working in unknown environments require the ability to determine their location while learning about obstacles located around them. In this paper a method of solving the SLAM problem that makes use of compressed occupancy grids is presented. The presented approach is an extension of the FastSLAM algorithm which stores a compressed form of the occupancy grid to reduce the amount of memory required to store the set of occupancy grids maintained by the particle filter. The performance of the algorithm is presented using experimental results obtained using a small inexpensive ground vehicle equipped with LiDAR, compass, and downward facing camera that provides the vehicle with visual odometry measurements. The presented results demonstrate that although with our approach the occupancy grid maintained by each particle uses only40%of the data needed to store the uncompressed occupancy grid, we can still achieve almost identical results to the approach where each particle filter stores the full occupancy grid.

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

scholarly journals Robust GNSS-denied localization for UAV using particle filter and visual odometry

2019 ◽  
Vol 30 (7-8) ◽  
pp. 1181-1190 ◽  
Author(s):  
Rokas Jurevičius ◽  
Virginijus Marcinkevičius ◽  
Justinas Šeibokas
Keyword(s):  
Particle Filter ◽  
Visual Odometry

TACTICAL ROUTE PLANNING: NEW ALGORITHMS FOR DECOMPOSING THE MAP

1996 ◽  
Vol 05 (01n02) ◽  
pp. 199-218 ◽  
Author(s):  
J.R. BENTON ◽  
S.S. IYENGAR ◽  
W. DENG ◽  
N. BRENER ◽  
V.S. SUBRAHMANIAN
Keyword(s):  
Data Structures ◽  
Fundamental Problem ◽  
Route Planning ◽  
Decomposition Methods ◽  
Search Algorithms ◽  
Experimental Results ◽  
New Approach ◽  
Robotic Vehicles ◽  
New Algorithms ◽  
Large Grid

This paper defines a new approach and investigates a fundamental problem in route planners. This capability is important for robotic vehicles(Martian Rovers, etc.) and for planning off-road military maneuvers. The emphasis throughout this paper will be on the design and analysis and hieiaichical implementation of our route planner. This work was motivated by anticipation of the need to search a grid of a trillion points for optimum routes. This cannot be done simply by scaling upward from the algorithms used to search a grid of 10,000 points. Algorithms sufficient for the small grid are totally inadequate for the large grid. Soon, the challenge will be to compute off-road routes more than 100 km long and with a one or two-meter grid. Previous efforts are reviewed and the data structures, decomposition methods and search algorithms are analyzed and limitations are discussed. A detailed discussion of a hieraichical implementation is provided and the experimental results are analyzed.

Pedestrian Tracking with HOG and Color Histogram Features

2012 ◽  
Vol 241-244 ◽  
pp. 498-501
Author(s):  
Lie Guo ◽  
Guang Xi Zhang ◽  
Ping Shu Ge ◽  
Lin Hui Li
Keyword(s):  
Particle Filter ◽  
Target Position ◽  
Color Histogram ◽  
Experimental Results ◽  
Pedestrian Tracking ◽  
Particle Sampling ◽  
Histograms Of Oriented Gradients ◽  
Initial Target

To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.

Indoor Self-Localization Using Multiple Magnetic Sensors

2019 ◽  
Vol 31 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Isaku Nagai ◽  
Jun Sakai ◽  
Keigo Watanabe ◽  
◽  
Keyword(s):  
Measurement Error ◽  
Particle Filter ◽  
Optical Sensor ◽  
Magnetic Sensors ◽  
The Self ◽  
Experimental Results ◽  
Map Matching ◽  
Multiple Sensors ◽  
Positioning Device

This study proposes an indoor self-localization for the estimation of the position and posture of an instrument using multiple magnetic sensors. First, a magnetic map for the localization is efficiently created using multiple sensors and a local positioning device made from an optical sensor and a gyroscope. For the localization estimating trajectories, the measurement error of the local positioning is corrected by matching it with the magnetic map. Our instrument is composed of six magnetic sensors, and the description of the self-localization details is based on the framework of a particle filter. The experimental results show better indoor path trajectories compared with a raw trajectory without map matching. The accuracy of the instrument using various numbers of magnetic sensors for the estimation is also investigated.

Dynamic Object Tracking Based on KAZE Features and Particle Filter

2014 ◽  
Vol 556-562 ◽  
pp. 2702-2706
Author(s):  
Ying Xia ◽  
Xin Hao Xu
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Scale Invariance ◽  
Experimental Results ◽  
Object Model ◽  
Dynamic Object ◽  
Color Features ◽  
Dynamic Tracking ◽  
Object Appearance ◽  
Accuracy And Stability

Accuracy and stability is crucial for dynamic object tracking. Considering the scale invariance, rotational invariance and strong anti-jamming capability of KAZE features, a method of dynamic object tracking based on KAZE features and particle filter is proposed. This method obtains the global color features of the dynamic object appearance and extracts its local KAZE features to construct the object model first, and then performs dynamic tracking by particle filter. Experimental results demonstrate the accuracy and stability of the proposed method.

Texture Feature-Based Particle Filter Video Tracking Using Cooccurrence Matrices

2013 ◽  
Vol 457-458 ◽  
pp. 1294-1297
Author(s):  
Tao Zhang ◽  
Yu Qing Chen ◽  
Xiang Yu Yu
Keyword(s):  
Particle Filter ◽  
Texture Feature ◽  
Video Tracking ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Color Feature ◽  
Texture Information ◽  
Feature Based

.In this paper, a video tracking approach based on particle filter is proposed. Texture information is used instead of color. In the proposed approach, gray cooccurrence matrices are used as the texture metric. Experimental results show that the proposed algorithm lead to better result than color feature-based particle filter-based video tracking algorithm and is an effective tool for complicated video tracking application.

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