Comparative Analysis of Approaches to Depth Map Generation for Robot Navigation

2020 ◽  
pp. 265-272
Author(s):  
Julia Rubtsova ◽  
Roman Iakovlev
Keyword(s):  
Comparative Analysis ◽  
Robot Navigation ◽  
Depth Map ◽  
Map Generation

Low complexity 3D depth map generation for stereo applications

2011 ◽  
Author(s):  
Cheng-An Chien ◽  
Cheng-Yen Chang ◽  
Jui-Sheng Lee ◽  
Jia-Hou Chang ◽  
Jiun-In Guo
Keyword(s):  
Depth Map ◽  
Low Complexity ◽  
Map Generation

scholarly journals Prioritized multi-view stereo depth map generation using confidence prediction

2018 ◽  
Vol 143 ◽  
pp. 167-180 ◽  
Author(s):  
Christian Mostegel ◽  
Friedrich Fraundorfer ◽  
Horst Bischof
Keyword(s):  
Depth Map ◽  
Map Generation

Angular Depth Map Generation for Gesture Recognition

2012 ◽  
Vol 3 (2) ◽  
pp. 1-8
Author(s):  
Pusik Park ◽  
Rakhimov Rustam Igorevich ◽  
Jongchan Choi ◽  
Dugki Min ◽  
Jongho Yoon
Keyword(s):  
Gesture Recognition ◽  
Depth Map ◽  
Map Generation

A New Approach to Sonar Based Indoor Mapping Localization

2005 ◽  
Author(s):  
A. Meghdari ◽  
K. Kobravi ◽  
H. Safyallah ◽  
M. Moeeni ◽  
Y. Khatami ◽  
...  
Keyword(s):  
Efficient Algorithm ◽  
Robot Navigation ◽  
Laser Scanner ◽  
Indoor Environments ◽  
Scanner Data ◽  
Narrow Beam ◽  
New Approach ◽  
Vehicle Localization ◽  
Environment Map ◽  
Map Generation

Vehicle localization and environment mapping are the most essential parts of the robot navigation in unknown environments. Since the problem of localization in indoor environments is directly related to the problem of online map generation, in this paper a new and efficient algorithm for simultaneous localization and map generation is proposed and novel results for real environments are achieved. This new algorithm interprets and validates the raw sonar measurements in first step, and applies them to the environment map in the next step. There are various adjustable parameters which make the algorithm flexible for different sonar types. This algorithm is efficient and is robust to sonar failure; if sonar does not work properly data can be discarded. These abilities make the algorithm efficient for sonar navigation in flat environments even by poor sonar and odometers perception data. This algorithm has the ability of matching with various types of sonar and even to be used with laser scanner data, whenever each laser scanner data is treated as multiple sonar detections with narrow beam detection patterns.

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