Research map / Probabilistic and scientific modeling / Variational and generative sampling
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TG-MCMC instead of Govindu | Pose graph initialization · EPFL Benchmark average | MRE: 0.230 -> 0.227 | Helped |
| Use TG-MCMC instead of Torsello | Pose graph initialization · EPFL Benchmark average | MTE: 0.090 -> 0.085 | Helped |
| Use TG-MCMC rather than the evaluated state-of-the-art methods | Synthetic pose graphs with increased noise or missing edges · Synthetic evaluations | rotation and translation error | Helped |
| Use the BOA integration order instead of other tested step orders (ablation) | TG-MCMC · Pose graph optimization | optimization results | Helped |
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