  In air-ground collaboration scenarios without GPS and prior maps, the
relative positioning of drones and unmanned ground vehicles (UGVs) has always
been a challenge. For a drone equipped with monocular camera and an UGV
equipped with LiDAR as an external sensor, we propose a robust and real-time
relative pose estimation method (LVCP) based on the tight coupling of vision
and LiDAR point cloud information, which does not require prior information
such as maps or precise initial poses. Given that large-scale point clouds
generated by 3D sensors has more accurate spatial geometric information than
the feature point cloud generated by image, we utilize LiDAR point clouds to
correct the drift in visual-inertial odometry (VIO) when the camera undergoes
significant shaking or the IMU has a low signal-to-noise ratio. To achieve
this, we propose a novel coarse-to-fine framework for LiDAR-vision
collaborative localization. In this framework, we construct point-plane
association based on spatial geometric information, and innovatively construct
a point-aided Bundle Adjustment (BA) problem as the backend to simultaneously
estimate the relative pose of the camera and LiDAR and correct the VIO drift.
In this process, we propose a particle swarm optimization (PSO) based sampling
algorithm to complete the coarse estimation of the current camera-LiDAR pose.
In this process, the initial pose of the camera used for sampling is obtained
based on VIO propagation, and the valid feature-plane association number (VFPN)
is used to trigger PSO-sampling process. Additionally, we propose a method that
combines Structure from Motion (SFM) and multi-level sampling to initialize the
algorithm, addressing the challenge of lacking initial values.
