Algorithm 1 Monte Carlo localization.
1:	Initialize particles ${\overline{\mathit{X}}}_t={\mathit{X}}_t={(}^w{\mathit{x}}_t^{\left[1\right]},{}^w{\mathit{x}}_t^{\left[2\right]},\cdots ,{}^w{\mathit{x}}_t^M)$
2:	for $m=1$ to M do
3:	Update particles with the motion model: ${}^w{\mathit{x}}_t^m=Motionmodel\left({}^w{x}_{t-1}^m\right)$
4:	Calculate the belief of each particle with the measurement model: $w^m=p\left(z_t\right|{}^w{\mathit{x}}_t^m)$
5:	${\overline{\mathit{X}}}_t={\overline{\mathit{X}}}_t+<{}^w{\mathit{x}}_t^m,w_t^m>$
6:	end for
7:	for $m=1$ to M do
8:	draw ${}^w{\mathit{x}}_t^m$ from ${\overline{\mathit{X}}}_t$ with probability ∝ $w_t^m$
9:	add ${}^w{\mathit{x}}_t^m$ to ${\mathit{X}}_t$
10:	end for
11:	for $m=1$ to M do
12:	if $w_t^m<\alpha$(a constant), re-initialize ${}^w{\mathit{x}}_t$
13:	end for
14:	return ${\mathit{X}}_t$