  For an autonomous vehicle, the ability to sense its surroundings and to build
an overall representation of the environment by fusing different sensor data
streams is fundamental. To this end, the poses of all sensors need to be
accurately determined. Traditional calibration methods are based on: 1) using
targets specifically designed for calibration purposes in controlled
environments, 2) optimizing a quality metric of the point clouds collected
while traversing an unknown but static environment, or 3) optimizing the match
among per-sensor incremental motion observations along a motion path fulfilling
special requirements. In real scenarios, however, the online applicability of
these methods can be limited, as they are typically highly dynamic, contain
degenerate paths, and require fast computations. In this paper, we propose an
approach that tackles some of these challenges by formulating the calibration
problem as a joint but structured optimization problem of all sensor
calibrations that takes as input a summary of the point cloud information
consisting of ground points and pole detections. We demonstrate the efficiency
and quality of the results of the proposed approach in a set of experiments
with LiDAR simulation and real data from an urban trip.
