Multi-sensor integration for unmanned terrain modeling

Abstract

State-of-the-art unmanned ground vehicles are capable of understanding and adapting to arbitrary road terrain for navigation. The robotic mobility platforms mounted with sensors detect and report security concerns for subsequent action. Often, the information based on the localization of the unmanned vehicle is not sufficient for deploying army resources. In such a scenario, a three dimensional (3D) map of the area that the ground vehicle has surveyed in its trajectory would provide apriori spatial knowledge for directing resources in an efficient manner. To that end, we propose a mobile, modular imaging system that incorporates multi-modal sensors for mapping unstructured arbitrary terrain. Our proposed system leverages 3D laser-range sensors, video cameras, global positioning systems (GPS) and inertial measurement units (IMU) towards the generation of photo-realistic, geometrically accurate, geo-referenced 3D terrain models. Based on the summary of the state-of-the-art systems, we address the need and hence several challenges in the real-time deployment, integration and visualization of data from multiple sensors. We document design issues concerning each of these sensors and present a simple temporal alignment method to integrate multi-sensor data into textured 3D models. These 3D models, in addition to serving as apriori for path planning, can also be used in simulators that study vehicle-terrain interaction. Furthermore, we show our 3D models possessing the required accuracy even for crack detection towards road surface inspection in airfields and highways.

Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface

“…However, they allocated only one color per voxel, which caused distortions. Sukumar et al [ 14 ] integrated sensed datasets into a texture mesh for terrain reconstruction. However, it is difficult for these systems to process large-scale datasets of the kind obtained in outdoor environments in real time.…”

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

“…Conventionally, the voxel map [16] and textured mesh [2] have been applied for this terrain modeling.…”

“…An operator controls the mobile robot through a remote control system (RCS), which provides an immersive virtual environment to enable an understanding of terrain information [2–4]. The operator controls the mobile robot by navigating and interacting with real environments without collisions or encountering other dangers [5].…”

Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes

“…Rovira-Más [ 13 ] proposed a density grid for 3D reconstruction from information obtained from stereo cameras, a localization sensor, and an inertial measurement unit. Sukumar [ 3 ] provided a convenient visualization method by integrating sensed datasets into a textured terrain mesh. However, it is difficult for these systems to process the large datasets obtained in outdoor environments and achieve on-line rendering.…”

“…Object segmentation and classification are widely researched topics in surveying, mapping, and autonomous navigation by mobile robots [ 1 , 2 ]. These techniques allow a robot to navigate through and interact with its environment by providing quickly accessible and accurate information regarding the surrounding terrain [ 3 ].…”