Name
Abstract | ||
|---|---|---|
This paper proposes a navigation algorithm that provides a low-latency solution while estimating the full nonlinear navigation state. Our approach uses Sliding-Window Factor Graphs, which extend existing incremental smoothing methods to operate on the subset of measurements and states that exist inside a sliding time window. We split the estimation into a fast short-term smoother, a slower but fully global smoother, and a shared map of 3D landmarks. A novel three-stage visual feature model is presented that takes advantage of both smoothers to optimize the 3D landmark map, while minimizing the computation required for processing tracked features in the short-term smoother. This three-stage model is formulated based on the maturity of the estimation of the 3D location of the underlying landmark in the map. Long-range associations are used as global measurements from matured landmarks in the short-term smoother and loop closure constraints in the long-term smoother. Experimental results demonstrate our approach provides highly-accurate solutions on large-scale real data sets using multiple sensors in GPS-denied settings. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICRA.2013.6630555 | ICRA |
Keywords | Field | DocType |
optimisation,3d landmark map optimization,robust vision-aided navigation,incremental smoothing methods,global measurements,long-range associations,smoothing methods,loop closure constraints,state estimation,global smoother,shared 3d landmark map,nonlinear estimation,3d location estimation,sliding time window,low-latency solution,feature extraction,fast short-term smoother,sliding-window factor graphs,long-term smoother,gps-denied settings,slam (robots),nonlinear navigation state estimation,large-scale real data sets,graph theory,tracked feature processing,three-stage visual feature model,multiple sensor data sets,navigation algorithm,measurement subset,sensor fusion,robot vision,sensors,solid modeling,navigation,estimation | Factor graph,Graph theory,Computer vision,Sliding window protocol,Computer science,Control theory,Sensor fusion,Feature extraction,Smoothing,Artificial intelligence,Landmark,Computation | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-4673-5641-1 | 13 | 0.66 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han-Pang Chiu | 1 | 94 | 10.83 |
| Stephen Williams | 2 | 13 | 0.66 |
| Frank Dellaert | 3 | 5242 | 438.33 |
| Supun Samarasekera | 4 | 792 | 85.72 |
| Rakesh Kumar | 5 | 1923 | 157.44 |