  In this paper we investigate a tightly coupled Lidar-Inertia Odometry and
Mapping (LIOM) scheme, with the capability to incorporate multiple lidars with
complementary field of view (FOV). In essence, we devise a time-synchronized
scheme to combine extracted features from separate lidars into a single
pointcloud, which is then used to construct a local map and compute the
feature-map matching (FMM) coefficients. These coefficients, along with the IMU
preinteration observations, are then used to construct a factor graph that will
be optimized to produce an estimate of the sliding window trajectory. We also
propose a key frame-based map management strategy to marginalize certain poses
and pointclouds in the sliding window to grow a global map, which is used to
assemble the local map in the later stage. The use of multiple lidars with
complementary FOV and the global map ensures that our estimate has low drift
and can sustain good localization in situations where single lidar use gives
poor result, or even fails to work. Multi-thread computation implementations
are also adopted to fractionally cut down the computation time and ensure
real-time performance. We demonstrate the efficacy of our system via a series
of experiments on public datasets collected from an aerial vehicle.
