Automatic generation of topological indoor maps for real-time map-based localization and tracking

A real-time map-building system is proposed for an autonomous underwater vehicle (AUV) to build a map of an unknown underwater environment. The system, using the AUV's onboard sensor information, includes a neurodynamics model proposed for complete coverage path planning and an evidence theoretic method proposed for map building. The complete coverage of the environment guarantees that the AUV can acquire adequate environment information. The evidence theory is used to handle the noise and uncertainty of the sensor data. The AUV dynamically plans its path with obstacle avoidance through the landscape of neural activity. Concurrently, real-time sensor data are “fused” into a two-dimensional (2D) occupancy grid map of the environment using evidence inference rule based on the Dempster-Shafer theory. Simulation results show a good quality of map-building capabilities and path-planning behaviors of the AUV.

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Research on parallelized real-time map matching algorithm for massive GPS data

Cluster Computing ◽

10.1007/s10586-017-0869-5 ◽

2017 ◽

Vol 20 (2) ◽

pp. 1123-1134 ◽

Cited By ~ 7

Author(s):

Hongyu Wang ◽

Jin Li ◽

Zhenshan Hou ◽

Ruochen Fang ◽

Wenbo Mei ◽

...

Keyword(s):

Real Time ◽

Gps Data ◽

Map Matching ◽

Matching Algorithm ◽

Time Map

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Near Real-time Map Building with Multi-class Image Set Labeling and Classification of Road Conditions Using Convolutional Neural Networks

Applied Artificial Intelligence ◽

10.1080/08839514.2021.1935590 ◽

2021 ◽

pp. 1-31

Author(s):

Sheela Ramanna ◽

Cenker Sengoz ◽

Scott Kehler ◽

Dat Pham

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Map Building ◽

Time Map ◽

Image Set

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An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

10.54216/jcim.060203 ◽

2021 ◽

pp. 101-107

Author(s):

Mohammad Alshehri ◽

Keyword(s):

Real Time ◽

Indoor Localization ◽

Initial Position ◽

Swarm Optimization ◽

The Real ◽

Assisted Navigation ◽

Glowworm Swarm Optimization ◽

Tracking Process ◽

Wide Range ◽

Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

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