Dynamic Planning Pipeline for Indoor Inspection Flights

In this report a basic pipeline for planning and operating an indoor drone flight is presented and evaluated in detail. We introduce the structure and interface considerations of a Planner Manager enabling autonomous indoor flights. The interaction routines of different planners are introduced in detail before we evaluate the system in both simulation and real test flights. We show that the system is capable of managing the typical building blocks of a mobile robotics system. Most of the components can be swapped easily to allow for rapid prototyping without the need to rework the whole pipeline

Structural Inspection Planning for Mobile Robots

In this report we present a pipeline for static coverage planning of known objects, which is an important task in the field of mobile robot based inspection. We analyse the main components of the Structural Inspection Planner [1] and embed an improved implementation into a autonomous flight pipeline for UAVs. Triangle mesh models serve as input for an initial viewpoint sampling. Inspection quality and path length are optimized by formulating the viewpoint sampling as constraint QP. We thoroughly evaluate the ROS-based inspection pipeline on synthetic and real models using a Gazebo simulation. Our experimental evaluation shows that while an efficient inspection trajectory could be generated for most of the tested models, the result is very dependent on regular and well formed input models

Efficient Global Occupancy Mapping for Mobile Robots using OpenVDB

In this work we present a fast occupancy map building approach based on the VDB datastructure. Existing log-odds based occupancy mapping systems are often not able to keep up with the high point densities and framerates of modern sensors. Therefore, we suggest a highly optimized approach based on a modern datastructure coming from a computer graphic background. A multithreaded insertion scheme allows occupancy map building at unprecedented speed. Multiple optimizations allow for a customizable tradeoff between runtime and map quality. We first demonstrate the effectiveness of the approach quantitatively on a set of ablation studies and typical benchmark sets, before we practically demonstrate the system using a legged robot and a UAV.Comment: 6 pages, presented in Agile Robotics Workshop at IROS202

The GOOSE Dataset for Perception in Unstructured Environments

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap, we present the German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset specifically designed for unstructured outdoor environments. The GOOSE dataset incorporates 10 000 labeled pairs of images and point clouds, which are utilized to train a range of state-of-the-art segmentation models on both image and point cloud data. We open source the dataset, along with an ontology for unstructured terrain, as well as dataset standards and guidelines. This initiative aims to establish a common framework, enabling the seamless inclusion of existing datasets and a fast way to enhance the perception capabilities of various robots operating in unstructured environments. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://goose-dataset.de/.Comment: Preprint; Submitted to IEEE for revie