  Micro-aerial vehicles (MAVs) are becoming ubiquitous across multiple
industries and application domains. Lightweight MAVs with only an onboard
flight controller and a minimal sensor suite (e.g., IMU, vision, and vertical
ranging sensors) have potential as mobile and easily deployable sensing
platforms. When deployed from a ground robot, a key parameter is a relative
localization between the ground robot and the MAV. This paper proposes a novel
method for tracking MAVs in lidar point clouds. In lidar point clouds, we
consider the speed and distance of the MAV to actively adapt the lidar's frame
integration time and, in essence, the density and size of the point cloud to be
processed. We show that this method enables more persistent and robust tracking
when the speed of the MAV or its distance to the tracking sensor changes. In
addition, we propose a multi-modal tracking method that relies on
high-frequency scans for accurate state estimation, lower-frequency scans for
robust and persistent tracking, and sub-Hz processing for trajectory and object
identification. These three integration and processing modalities allow for an
overall accurate and robust MAV tracking while ensuring the object being
tracked meets shape and size constraints.
