Скачать или смотреть Running tightly coupled GPS/INS/Lidar integrated navigation system

We have proposed a framework for tightly-coupled lidar
inertial odometry via smoothing and mapping, coped with
orientation-estimation algorithm for performing real-time
state estimation and mapping in complex environments. By
formulating lidar-inertial odometry atop a factor graph,
proposed framework is especially suitable for multi-sensor
fusion. Additional sensor measurements can easily be
incorporated into the framework as new factors. Sensors that
provide absolute measurements, such as a GPS, compass, or
altimeter, can be used to eliminate the drift of lidar inertial
odometry that accumulates over long durations, or in featurepoor environments. Place recognition can also be easily
incorporated into the system. To improve the real-time
performance of the system, keyframes are selectively added to
the factor graph, and new keyframes are registered only to a
fixed-size set of sub-keyframes when both lidar odometry and
loop closure factors are generated. This lower the processing
time for scan-matching, as it scan at a local scale rather than a
global scale. The proposed method is thoroughly evaluated on
datasets gathered across a variety of environments. The results
show that proposed framework can achieve high accuracy at
low computing cost. Future work involves integrating the
proposed system on unmanned ground vehicles applications.