Extrinsic Calibration of Lidar and Camera with Polygon

Fusion of heterogeneous exteroceptive sensors is the most efficient and effective path to the representation of the environment precisely, as it can compromise various drawbacks of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems is the prerequisite of fusing the multi-sensor information. Researchers have proposed several approaches to estimate the extrinsic parameters. However, these approaches neither rely on human interventions or specifically designed auxiliary object or do not provide the library which makes it hard to test or benchmark. In this paper, we propose a novel extrinsic calibration approach for the extrinsic calibration of a Lidar (Laser Range Finder) and a camera which only based on a polygon board and we offer the relevant tools. In this paper, we firstly track and extract the target polygon from both the image and point-cloud. Then we try to match the polygon between the 2D and 3D feature spaces. With the associated polygon, we are able to get multiple constraints to optimize the extrinsic parameters. At the end, we validate our approach by four configurations, including the simulation, t6/32-beam Lidar and 100-line MEMS-Lidar. The outcome indicates high-precision extrinsic calibration performance.