  This paper uses the smoothing and mapping framework to solve the SLAM problem
in indoor environments; focusing on how some key issues such as feature
extraction and data association can be handled by applying probabilistic
techniques. For feature extraction, an odds ratio approach to find multiple
lines from laser scans is proposed, this criterion allows to decide which model
must be merged and to output the best number of models. In addition, to solve
the data association problem a method based on the segments of each line is
proposed. Experimental results show that high quality indoor maps can be
obtained from noisy data
