On measuring the accuracy of SLAM algorithms

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.     

Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper, we present adaptive techniques for reducing this number in a RBPF for learning grid maps. We propose an approach to compute an accurate proposal distribution, taking into account not only the movement of the robot, but also the most recent observation. This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out resampling operations, which seriously reduces the problem of particle depletion. Experimental results carried out with real mobile robots in large-scale indoor, as well as outdoor, environments illustrate the advantages of our methods over previous approaches     

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.     

Robotic weed control using automated weed and crop classification

Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant‐time weed detection systems to achieve real‐time treatment, which forecloses the implementation of more capable but time‐consuming algorithms, for example, learning‐based methods. In this paper, a nonoverlapping multicamera system is applied to provide flexibility for the weed control system in dealing with the indeterminate classification delays. The design, implementation, and testing of our proposed modular weed control unit with mechanical and chemical weeding tools are presented. A framework that performs naive Bayes filtering, 3D direct intra‐ and inter‐camera visual tracking, and predictive control, while integrating state‐of‐the‐art crop/weed detection algorithms, is developed to guide the tools to achieve high‐precision weed removal. The experimental results show that our proposed fully operational weed control system is capable of performing selective mechanical as well as chemical in‐row weeding with indeterminate detection delays in different terrain conditions and crop growth stages.     

Carbon nanotubes in macrophages: imaging and chemical analysis by X-ray fluorescence microscopy

X-ray fluorescence microscopy (microXRF) is applied for the first time to study macrophages exposed to unpurified and purified single-walled (SW) and multiwalled (MW) carbon nanotubes (CNT). Investigating chemical elemental distributions allows one to (i) image nanotube localization within a cell and (ii) detect chemical modification of the cell after CNT internalization. An excess of calcium is detected for cells exposed to unpurified SWCNT and MWCNT and related toxicological assays are discussed.     

Coating carbon nanotubes with a polystyrene-based polymer protects against pulmonary toxicity

Background  

carbon nanotubes (CNT) can have adverse effects on health. Therefore, minimizing the risk associated with CNT exposure is of crucial importance. The aim of this work was to evaluate if coating multi-walled CNT (MWCNT) with polymers could modify their toxicity, thus representing a useful strategy to decrease adverse health effects of CNT. We used industrially-produced MWCNT uncoated (NT1) or coated (50/50 wt%) with acid-based (NT2) or polystyrene-based (NT3) polymer, and exposed murine macrophages (RAW 264.7 cell line) or Balb/c mice by intratracheal administration. Biological experiments were performed both in vitro and in vivo, examining time- and dose-dependent effects of CNT, in terms of cytotoxicity, expression of genes and proteins related to oxidative stress, inflammation and tissue remodeling, cell and lung tissue morphology (optical and transmission electron microscopy), and bronchoalveolar lavage fluid content analysis.     

Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

Conventional farming still relies on large quantities of agrochemicals for weed management which have several negative side‐effects on the environment. Autonomous robots offer the potential to reduce the amount of chemicals applied, as robots can monitor and treat each plant in the field individually and thereby circumventing the uniform chemical treatment of the whole field. Such agricultural robots need the ability to identify individual crops and weeds in the field using sensor data and must additionally select effective treatment methods based on the type of weed. For example, certain types of weeds can only be effectively treated mechanically due to their resistance to herbicides, whereas other types can be treated trough selective spraying. In this article, we present a novel system that provides the necessary information for effective plant‐specific treatment. It estimates the stem location for weeds, which enables the robots to perform precise mechanical treatment, and at the same time provides the pixel‐accurate area covered by weeds for treatment through selective spraying. The major challenge in developing such a system is the large variability in the visual appearance that occurs in different fields. Thus, an effective classification system has to robustly handle substantial environmental changes including varying weed pressure, various weed types, different growth stages, changing visual appearance of the plants and the soil. Our approach uses an end‐to‐end trainable fully convolutional network that simultaneously estimates plant stem positions as well as the spatial extent of crop plants and weeds. It jointly learns how to detect the stems and the pixel‐wise semantic segmentation and incorporates spatial information by considering image sequences of local field strips. The jointly learned feature representation for both tasks furthermore exploits the crop arrangement information that is often present in crop fields. This information is considered even if it is only observable from the image sequences and not a single image. Such image sequences, as typically provided by robots navigating over the field along crop rows, enable our approach to robustly estimate the semantic segmentation and stem positions despite the large variations encountered in different fields. We implemented and thoroughly tested our approach on images from multiple farms in different countries. The experiments show that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to deploy such systems in the real world. Compared to state‐of‐the‐art approaches, our approach generalizes well to unseen fields and not only substantially improves the stem detection accuracy, that is, distinguishing crop and weed stems, but also improves the semantic segmentation performance.     

TOURBOT and WebFAIR: Web-operated mobile robots for tele-presence in populated exhibitions

This paper presents a number of techniques that are needed for realizing Web-operated mobile robots. These techniques include effective map-building capabilities, a method for obstacle avoidance based on a combination of range and visual information, and advanced Web and onboard robot interfaces. In addition to video streams, the system provides high-resolution virtual reality visualizations that also include the people in the vicinity of the robot. This increases the flexibility of the interface and simultaneously allows a user to understand the navigation actions of the robot. The techniques described in this article have been successfully deployed within the EU-funded projects TOURBOT and WebFAIR, which aimed to develop interactive tour-guided robots able to serve Web as well as on-site visitors. Technical developments in the framework of these projects have resulted in robust and reliable systems that have been demonstrated and validated in real-world conditions. Equally important, the system setup time has been drastically reduced, facilitating its porting to new environments.