Improvements to Target-Based 3D LiDAR to Camera Calibration

Abstract

The homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm translation errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors in their distance measurements. This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession.

References in corpus (5)

• Faster and better: a machine learning approach to corner detection
• LiDAR-Camera Calibration using 3D-3D Point correspondences
• Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping
• A new approach (extra vertex) and generalization of Shoelace Algorithm usage in convex polygon (Point-in-Polygon)
• A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Cited by in corpus (9)

• LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
• ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems
• Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference
• Intensity Image-based LiDAR Fiducial Marker System
• Optimal Target Shape for LiDAR Pose Estimation
• Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain
• Global Unifying Intrinsic Calibration for Spinning and Solid-State LiDARs
• L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration
• PLK-Calib: Single-shot and Target-less LiDAR-Camera Extrinsic Calibration using Plücker Lines