  Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in
large-scale, real-world scenarios. However, they commonly have a high latency
due to the expensive data association and nonlinear optimization. This paper
demonstrates that actively selecting a subset of features significantly
improves both the accuracy and efficiency of an L-SLAM system. We formulate the
feature selection as a combinatorial optimization problem under a cardinality
constraint to preserve the information matrix's spectral attributes. The
stochastic-greedy algorithm is applied to approximate the optimal results in
real-time. To avoid ill-conditioned estimation, we also propose a general
strategy to evaluate the environment's degeneracy and modify the feature number
online. The proposed feature selector is integrated into a multi-LiDAR SLAM
system. We validate this enhanced system with extensive experiments covering
various scenarios on two sensor setups and computation platforms. We show that
our approach exhibits low localization error and speedup compared to the
state-of-the-art L-SLAM systems. To benefit the community, we have released the
source code: https://ram-lab.com/file/site/m-loam.
