scholarly journals Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

Coding Theory ◽  
2020 ◽  
Author(s):  
Satyarth Praveen
Keyword(s):  
Stereo Vision ◽  
Depth Estimation

Stereo Vision Specific Observation Model for EKF-Based SLAM

2013 ◽  
Vol 373-375 ◽  
pp. 238-241
Author(s):  
Yao Chang Chen ◽  
Ta Ming Shih ◽  
Chung Ho Wang
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Stereo Vision ◽  
Linear Problem ◽  
Traditional Approach ◽  
Depth Estimation ◽  
Observation Model ◽  
Uncertainty Distribution ◽  
Localization And Mapping ◽  
Projection Equations

This work addresses a new probabilistic observation model for a stereo simultaneous localization and mapping (SLAM) system within the standard Extended-Kalman filter (EKF) framework. The observation modal was derived by using the inverse depth parameterization as the landmark modal, and contributes to both bearing and range information into the EKF estimation. In this way the inherently non-linear problem cause by the projection equations is resolved and real depth uncertainty distribution of landmarks features can be accurately estimated. The system was demonstrated with real-world outdoor data. Analysis results show landmark feature depth estimation is more stable and the uncertainty noise converges faster than the traditional approach.

scholarly journals Stereo Vision Depth Estimation Methods for Robotic Applications

Robotics ◽  
2013 ◽  
pp. 1461-1481
Author(s):  
Lazaros Nalpantidis ◽  
Antonios Gasteratos
Keyword(s):  
Stereo Vision ◽  
Vision Research ◽  
Depth Estimation ◽  
Research Community ◽  
Estimation Methods ◽  
Autonomous Operation ◽  
Working Environments ◽  
Important Sense ◽  
New Challenges ◽  
Robotic Applications

Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for vision-based autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

Stereo Vision Depth Estimation Methods for Robotic Applications

2011 ◽  
pp. 397-417 ◽  
Author(s):  
Lazaros Nalpantidis ◽  
Antonios Gasteratos
Keyword(s):  
Stereo Vision ◽  
Vision Research ◽  
Depth Estimation ◽  
Research Community ◽  
Estimation Methods ◽  
Autonomous Operation ◽  
Working Environments ◽  
Important Sense ◽  
New Challenges ◽  
Robotic Applications

Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for vision-based autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

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