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Design references: - Factor Graphs for Robot Perception — Dellaert and Kaess: https://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf, §§2.2–2.3: measurement likelihoods, weighted residual costs and linearization. - iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree — Kaess et al.: https://www.cs.cmu.edu/~kaess/pub/Kaess12ijrr.pdf, §2: the nonlinear factor-graph estimation problem supplied to the solver.
Design references: - Factor Graphs for Robot Perception — Dellaert and Kaess: https://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf, §§5.4.2–5.4.3: incremental Bayes-tree updates and iSAM2. §5.3.2 provides the background for the intended fixed-lag estimation window. - iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree — Kaess et al.: https://www.cs.cmu.edu/~kaess/pub/Kaess12ijrr.pdf, §4.3 and Algorithm 8: the incremental update cycle. Section and algorithm numbering refer to this linked author manuscript.
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Add empty source and header scaffolding for the estimation core:
preintegration: accumulate IMU samples into relative motion and uncertainty for prediction and IMU constraints.factor_builders: construct built-in and expression factors from sensor measurements and noise models for iSAM2.estimation: initialize navigation, associate buffered measurements with states, and coordinate prediction and iSAM2 updates.Design references: