We describe contributions made to the Unreal gaming engine community supporting our development of algorithms that enable navigation in off-road and unstructured environments. Simulation of autonomous ground vehicles traversing complex 3D terrain requires accurate modeling of vehicle-terrain interaction. Widely used community gaming engines model this interaction well but do not include sensor model and other features found in open source tools. We have extended the Unreal engine to include models for LiDARs, IMU, and GNSS systems that are compatible with the Robot Operating System middle-ware. We have also added features that allow for the automated testing of off-road navigation algorithms.
We present an open source 3-dimensional 6 DOF (Degrees Of Freedom) point cloud based mapping package and online map manager for ROS (Robot Operating System) using PCL (Point Cloud Library) for use in autonomous navigation and localization in any environment. This system will be of interest to the robotics community as many mapping systems are either not sufficiently configured for 3D mapping in 6 DOF systems or are proprietary. The goal of this is to be able to produce maps which contain enough detail to be used for localization, as well as path planning, without sacrificing memory or speed. The mapping method consists of several key features described in detail: a novel method of initial localization and odometry estimation, a mapping registration method, a map storage method, and a map manager which handles map recall based on robot pose. Using a 3D LiDAR scanner and an IMU we are able to map in structured environments, and with addition of another odometry source able to complete mapping in unstructured environments with fewer features. The results of this work is an open source, extendable, environment independent mapping scheme and is well defined for all 6DOF. Verification of this system has been done in a simulation environment as well as real-world experiments.
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