Abstract
We describe a robust system for vision-based SLAM using a single
camera which runs in real-time, typically around 30 fps. The key
contribution is a novel utilisation of multi-resolution descriptors in
a coherent top-down framework. The resulting system provides superior
performance over previous methods in terms of robustness to erratic
motion, camera shake, and the ability to recover from measurement
loss. SLAM itself is implemented within an unscented Kalman filter
framework based on a constant position motion model, which is also
shown to provide further resilience to non-smooth camera
motion. Results are presented illustrating successful SLAM operation
for challenging hand-held camera movement within desktop
environments.
| Translated title of the contribution | Real-Time and Robust Monocular SLAM Using Predictive Multi-resolution Descriptors |
|---|---|
| Original language | English |
| Title of host publication | 2nd International Symposium on Visual Computing |
| Publication status | Published - 2006 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: 2nd International Symposium on Visual Computing
Other identifier: 2000571