Visual-Inertial-Laser-Lidar (VILL) SLAM: Real-Time Dense RGB-D Mapping for Pipe Environments

Abstract

Robotic solutions for pipeline inspection promise enhancement of human labor by automating data acquisition for pipe condition assessments, which are vital for the early detection of pipe anomalies and the prevention of hazardous leakages and explosions. Through simultaneous localization and mapping (SLAM), colorized 3D reconstructions of the pipe's inner surface can be generated, providing a more comprehensive digital record of the pipes compared to conventional vision-only inspection. Designed for generic environments, most SLAM methods suffer limited accuracy and substantial accumulative drift in confined and featureless spaces such as pipelines, due to a lack of suitable sensor hardware and state estimation techniques. In this research, we present VILL-SLAM: a dense RGB-D SLAM algorithm that combines a monocular camera (V), an inertial sensor (I), a ring-shaped laser profiler (L), and a Lidar (L) into a compact sensor package optimized for in-pipe operations. By fusing complementary visual and depth information from the color camera, laser profiling, and Lidar measurement, our method overcomes the challenges of metric scale mapping in conventional SLAM methods, despite its monocular configuration. To further improve localization accuracy, we utilize the pipe geometry to formulate two unique optimization factors that effectively constrain odometer drift. To validate our method, we conducted real-world experiments in physical pipes, comparing the performance of our approach against other state-of-the-art algorithms. The proposed SLAM framework achieved 6.6 times drift improvement with 0.84% mean odometry drift over 22 meters and a mean pointwise 3D scanning error of 0.88mm in 12-inch diameter pipes. This research represents a significant advancement in miniature in-pipe inspection, localization, and mapping sensing techniques. It has the potential to become a core enabling technology for the next generation of highly capable in-pipe robots, capable of reconstructing photo-realistic 3D pipe scans and providing disruptive pipe locating and georeferencing capabilities.