Abstract
Sensor fusion is one of the main challenges in self driving and robotics applications. In this paper we propose an automatic, online and target-less camera-Lidar extrinsic calibration approach. We adopt a structure from motion (SfM) method to generate 3D point clouds from the camera data which can be matched to the Lidar point clouds; thus, we address the extrinsic calibration problem as a registration task in the 3D domain. The core step of the approach is a two-stage transformation estimation: First, we introduce an object level coarse alignment algorithm operating in the Hough space to transform the SfM-based and the Lidar point clouds into a common coordinate system. Thereafter, we apply a control point based nonrigid transformation refinement step to register the point clouds more precisely. Finally, we calculate the correspondences between the 3D Lidar points and the pixels in the 2D camera domain. We evaluated the method in various real-life traffic scenarios in Budapest, Hungary. The results show that our proposed extrinsic calibration approach is able to provide accurate and robust parameter settings on-the-fly.
Highlights
Nowadays, state-of-the-art autonomous systems rely on wide range of sensors for environment perception such as optical cameras, radars and Lidars, efficient sensor fusion is a highly focused research topic in the fields of self-driving vehicles and robotics
We adopt a structure from motion (SfM) method to generate 3D point clouds from the camera data which can be matched to the Lidar point clouds; we address the extrinsic calibration problem as a registration task in the 3D domain
This paper proposed a new target-less, automatic camera-Lidar calibration approach which can be performed on-the-fly, i.e., during the driving without stopping the scanning platform
Summary
State-of-the-art autonomous systems rely on wide range of sensors for environment perception such as optical cameras, radars and Lidars, efficient sensor fusion is a highly focused research topic in the fields of self-driving vehicles and robotics. While real time Lidars, such as Velodyne’s rotating multi-beam (RMB) sensors provide accurate 3D geometric information with relatively low vertical resolution, optical cameras capture high resolution and high quality image sequences enabling to perceive low level details from the scene. A common problem with optical cameras is that extreme lighting conditions (such as dark, or strong sunlight) largely influence the captured image data, while Lidars are able to provide reliable information much less depending on external illumination and weather conditions. By simultaneous utilization of Lidar and camera sensors, accurate depth with detailed texture and color information can be obtained in parallel from the scenes. Accurate Lidar and camera calibration is an essential step to implement robust data fusion, related issues are extensively studied in the literature [1,2,3]. Existing calibration techniques can be grouped based on a variety of aspects [1]: based on the level of user interaction they can be semi- or fully automatic, methodologically we can distinguish target-based and target-less approaches, and in the term of operational requirements offline and online approaches can be defined (see Section 2)
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