  LiDAR is playing a more and more essential role in autonomous driving
vehicles for objection detection, self localization and mapping. A single LiDAR
frequently suffers from hardware failure (e.g., temporary loss of connection)
due to the harsh vehicle environment (e.g., temperature, vibration, etc.), or
performance degradation due to the lack of sufficient geometry features,
especially for solid-state LiDARs with small field of view (FoV). To improve
the system robustness and performance in self-localization and mapping, we
develop a decentralized framework for simultaneous calibration, localization
and mapping with multiple LiDARs. Our proposed framework is based on an
extended Kalman filter (EKF), but is specially formulated for decentralized
implementation. Such an implementation could potentially distribute the
intensive computation among smaller computing devices or resources dedicated
for each LiDAR and remove the single point of failure problem. Then this
decentralized formulation is implemented on an unmanned ground vehicle (UGV)
carrying 5 low-cost LiDARs and moving at $1.3m/s$ in urban environments.
Experiment results show that the proposed method can successfully and
simultaneously estimate the vehicle state (i.e., pose and velocity) and all
LiDAR extrinsic parameters. The localization accuracy is up to 0.2% on the two
datasets we collected. To share our findings and to make contributions to the
community, meanwhile enable the readers to verify our work, we will release all
our source codes and hardware design blueprint on our Github.
