[AMR] Finished update

electives/amr/parts/04_vision/00_keypoints.tex

@@ -26,3 +26,6 @@ Then apply Laplacian Operator $\nabla_\text{norm}^2 L = t\left( \frac{\partial^2
 \bi{Diff. of Gaussians} (DoG): $\Delta L = L(x, y, t) - L(x, y, kt)$
 
 \bi{SIFT Detector} \bi{(1)} Subsample + Blur \bi{(2)} DoG on each res. image \bi{(3)} Keypoints extrema in DoG pyramid
+
+\bi{BRISK} / binary descriptors: Compare pixel intensities at fixed sampling pattern around keypoint. Match by Hamming distance (very fast),
+\bi{SuperPoint}: CNN learns detector + descriptor

electives/amr/parts/04_vision/01_bootstrapping.tex

@@ -3,8 +3,11 @@
 \bi{PnP Problem} {\scriptsize Perspective $n$-Point}
 Find sol. for camera pose \textit{directly}
 
-\bi{RANSAC} {\scriptsize RANdom SAmpling Consensus} for find. outliers \& correct
+\bi{RANSAC} {\scriptsize RANdom SAmpling Consensus} Model estimation, for find. outliers \& correct.
 Also great for finding an initial guess for pose. (due to robustness)
+For $N$ iteration: sample min set, fit model, count inliers (reprojection error $<$ threshold $t$), keep best, optionally refit on inliers.
+More outliers $\Rightarrow$ more iterations.
+
 
 \bi{Stereo Triang.} Given two rays (known poses for points in 2D).
 Find good point in 3D. Fast sol: \bi{Midpoint Method}:

electives/amr/parts/04_vision/03_mapping.tex

@@ -30,4 +30,14 @@ $l(o_j | \vec{x}_{R, 1 : k}, \vec{z}_{1 : k})$ =
 \shade{gray}{In 3D} 3D voxel $j$ as signed dist. $s$ and weight $w$, update:
 $\displaystyle s_k = \frac{w_{k - 1} s_{k - 1} + \tilde{s}_k}{w_{k - 1} + 1}$ with $w_k = \min(w_{\max}, w_{k - 1} + 1)$
 
-\bi{Impl.} Using HashTables or octree
+\bi{Implementation} Using Hash maps or octree (dense grid inefficient)
+
+
+\subsubsection{Iterative Closest Point}
+Build \textit{correspondences}: associate all live scan points $l_i$ to closest map points $m_i$. Error term:
+$\vec{e} = \vec{T}_{W L_l L_l} m_i - {_W}\vec{l}_i$.
+Minimize this via Gauss-Newton, then re-associate, iterate.
+
+\bi{Photometric}: $u_{KF} = \pi(\mat{T}_{WC_{KF}}^{-1} \mat{T}_{WC_l} \pi^{-1}(\vec{u}_{KF}, \mat{D}_{KF}[\vec{u}_{KF}]))$,
+error term $\vec{e} = \mat{I}_{KF}[\vec{u}_{KF}] - \mat{I}_L[\vec{u}_{L}]$,
+where all subscript $L$ are from live image, all subscript $KF$ key frame.