Strübbe, Simon: Insect-Inspired Visual Self-Motion Estimation. 2019
Inhalt
- Acknowledgements
- Eidesstattliche Versicherung
- Introduction and Overview
- The insect visual motion pathway
- The ommatidia and their photoreceptors
- The first visual neuropile
- The second visual neuropile
- The third visual neuropile
- Saccadic flight behavior of flying insects
- The utilization of optic flow by flying insects
- The two compared self-motion estimation methods and their commonalities
- The difference between the two self-motion methods
- Derivation and analysis of the adaptive matched filter approach
- Motion adaptation and the adaptive MFA
- Insect-Inspired Self-Motion Estimation with Dense Flow Fields
- Abstract
- Introduction
- Results
- The matched filter approach
- The Koenderink-van-Doorn (KvD) algorithm
- Alternative derivation and properties of the coupling matrix in the MFA
- The relationship between the MFA and the KvD algorithm
- The bias of the KvD algorithm
- An adaptive MFA
- Discussion
- Materials and Methods
- Numerical test and simulation
- Construction of a spherical field of view
- Numerical test of the bias of the KvD algorithm
- Simulation of the adaptive MFA
- Appendix
- Learning Depth Models for Egomotion Estimation with Dense Flow Fields
- Abstract
- Introduction
- Methods
- 3D models
- Implementation of the trajectories
- Implementation of the Lucas-Kanade motion detector
- Matched filter algorithms for egomotion estimation
- Results
- Characteristics of the Lukas-Kanade detector
- Learning of the depth model
- Comparison with fixed depth model
- Discussion
- Resume and Outlook
- Bibliography