diff toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/mk_gmux_robot_dbn.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/mk_gmux_robot_dbn.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,85 @@
+function [bnet, Unode, Snode, Lnodes, Rnode, Ynode, Lsnode] = ...
+    mk_gmux_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block)
+
+% Make DBN
+
+% S
+% | L1 -------> L1'
+% |  | L2 ----------> L2'
+% \  | /
+%  v v v
+%    Ls
+%    |
+%    v
+%    Y
+%    ^
+%    |
+%    R ------->  R'
+%    ^      
+%    |      
+%    U      
+%
+%
+% S is a switch, Ls is a deterministic gmux, Y = Ls-R,
+% R(t+1) = R(t) + U(t+1), L(t+1) = L(t)
+
+
+% number nodes topologically
+Snode = 1;
+Lnodes = 2:nlandmarks+1;
+Lsnode = nlandmarks+2;
+Unode = nlandmarks+3;
+Rnode = nlandmarks+4;
+Ynode = nlandmarks+5;
+
+nnodes = nlandmarks+5; 
+intra = zeros(nnodes, nnodes);
+intra([Snode Lnodes], Lsnode) =1;
+intra(Unode,Rnode)=1;
+intra([Rnode Lsnode], Ynode)=1;
+
+inter = zeros(nnodes, nnodes);
+inter(Rnode, Rnode)=1;
+for i=1:nlandmarks
+  inter(Lnodes(i), Lnodes(i))=1;
+end
+
+Lsz = 2; % (x y) posn of landmark
+Rsz = 2; % (x y) posn of robot
+Ysz = 2; % relative distance
+Usz = 2; % (dx dy) ctrl
+Ssz = nlandmarks; % can switch between any landmark
+
+ns = zeros(1,nnodes);
+ns(Snode) = Ssz;
+ns(Lnodes) = Lsz;
+ns(Lsnode) = Lsz;
+ns(Ynode) = Ysz;
+ns(Rnode) = Rsz;
+ns(Ynode) = Usz;
+ns(Unode) = Usz;
+
+bnet = mk_dbn(intra, inter, ns, 'discrete', Snode, 'observed', [Snode Ynode Unode]);
+
+
+bnet.CPD{Snode} = root_CPD(bnet, Snode); % always observed
+bnet.CPD{Unode} = root_CPD(bnet, Unode); % always observed
+for i=1:nlandmarks
+  bi = landmark_block(:,i);
+  bnet.CPD{Lnodes(i)} = gaussian_CPD(bnet, Lnodes(i), 'mean', init_x(bi), 'cov', init_V(bi,bi));
+end
+bi = robot_block;
+bnet.CPD{Rnode} = gaussian_CPD(bnet, Rnode, 'mean', init_x(bi), 'cov', init_V(bi,bi), 'weights', eye(2));
+bnet.CPD{Lsnode} = gmux_CPD(bnet, Lsnode, 'cov', repmat(zeros(Lsz,Lsz), [1 1 nlandmarks]), ...
+			    'weights', repmat(eye(Lsz,Lsz), [1 1 nlandmarks]));
+W = [eye(2) -eye(2)]; % Y = Ls - R, where Ls is the lower-numbered parent
+bnet.CPD{Ynode} = gaussian_CPD(bnet, Ynode, 'mean', zeros(Ysz,1), 'cov', R, 'weights', W);
+
+% slice 2
+eclass = bnet.equiv_class;
+W = [eye(2) eye(2)]; % R(t) = R(t-1) + U(t), where R(t-1) is the lower-numbered parent
+bnet.CPD{eclass(Rnode,2)} = gaussian_CPD(bnet, Rnode+nnodes, 'mean', zeros(Rsz,1), 'cov', Q, 'weights', W);
+for i=1:nlandmarks
+  bnet.CPD{eclass(Lnodes(i), 2)} = gaussian_CPD(bnet, Lnodes(i)+nnodes, 'mean', zeros(2,1), ...
+						   'cov', zeros(2,2), 'weights', eye(2));
+end