An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping

Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixel-wise labelled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pre-trained on publicly available datasets. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model re-training. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labelled for model re-training. Experimental results on real-world data and in a photorealistic simulation show that our framework maximises model performance and drastically reduces labelling efforts. Our map-based planners outperform state-of-the-art local planning.Comment: 18 pages, 24 figure

Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots

Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. Obtaining such 3D information is non-trivial as agricultural environments are often repetitive and cluttered, and one has to account for the partial observability of fruit and plants. In this paper, we address the problem of jointly estimating complete 3D shapes of fruit and their pose in a 3D multi-resolution map built by a mobile robot. To this end, we propose an online multi-resolution panoptic mapping system where regions of interest are represented with a higher resolution. We exploit data to learn a general fruit shape representation that we use at inference time together with an occlusion-aware differentiable rendering pipeline to complete partial fruit observations and estimate the 7 DoF pose of each fruit in the map. The experiments presented in this paper evaluated both in the controlled environment and in a commercial greenhouse, show that our novel algorithm yields higher completion and pose estimation accuracy than existing methods, with an improvement of 41% in completion accuracy and 52% in pose estimation accuracy while keeping a low inference time of 0.6s in average. Codes are available at: https://github.com/PRBonn/HortiMapping.Comment: 8 pages, IROS 202

TRAIL receptors are expressed in both malignant and stromal cells in pancreatic ductal adenocarcinoma

: This study assesses the expression of all TNF-related apoptosis-inducing ligand (TRAIL) receptors in pancreatic ductal adenocarcinoma (PDAC) tumor tissue. We aimed to include TRAIL receptor expression as an inclusion parameter in a future clinical study using a TRAIL-based therapy approach for PDAC patients. Considering the emerging influence of PDAC desmoplastic stroma on the efficacy of anti-PDAC therapies, this analysis was extended to tumor stromal cells. Additionally, we performed PDAC stroma characterization. Our retrospective cohort study (N=50) included patients with histologically confirmed PDAC who underwent surgery. The expression of TRAIL receptors (DR4, DR5, DcR1, DcR2, and OPG) in tumor and stromal cells was evaluated by immunohistochemistry (IHC). The amount of tumor stroma was assessed by anti-vimentin IHC and Mallory's trichrome staining. The prognostic impact was determined by the univariate Cox proportional hazards regression model. An extensive expression of functional receptors DR4 and DR5 and a variable expression of decoy receptors were detected in PDAC tumor and stromal cells. Functional receptors were detected also in metastatic tumor and stromal cells. A poor prognosis was associated with low or absent expression of decoy receptors in tumor cells of primary PDAC. After assessing that almost 80% of tumor mass was composed of stroma, we correlated a cellular-dense stroma in primary PDAC with reduced relapse-free survival. We demonstrated that TRAIL functional receptors are widely expressed in PDAC, representing a promising target for TRAIL-based therapies. Further, we demonstrated that a low expression of DcR1 and the absence of OPG in tumor cells, as well as a cellular-dense tumor stroma, could negatively impact the prognosis of PDAC patients

Registration of spatio-temporal point clouds of plants for phenotyping.

Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level

A portable da Vinci simulator in virtual reality

Research activity in Minimally Invasive Robotic Surgery (MIRS) has gained a considerable momentum in the last years, due to the availability of reliable and clinically relevant research platforms like the da Vinci Research Kit (dVRK). However, despite the wide sharing of the dVRK in the research community, access to the platform remains limited because of high maintenance costs and difficulty in replacing components. In this work we complete a robotic simulator of the dVRK, previously developed by our group, with cheap haptic interfaces and an Oculus Rift to replicate and extend the functionalities of the Master console. The complete system represents an efficient, safe and low-cost tool, useful to design and validate new surgical instruments and control strategies, as well as provide an easyto-access educational tool to students

Distributed Energy Resources On-Board Cruise Ships: Integration into the Ship Design Process

An intense innovation activity is characterizing the energy system solutions on board ships, especially in the case of large passenger ships due to the significant total amount of installed power and the variegate typology of electrical loads. In the paper the solution of a distributed energy system will be considered for a 140.000+ GT cruise ship, in the perspective of a superior performance in terms of safety and energy efficiency. The target is to overcome the traditional concept of power generation based on large diesel Gensets located in few compartments. The innovative proposal is to integrate it, for the hotel needs, with a greater number of power generation units (but of a smaller size), properly distributed on board. For the application, a suitable reference cruise ship vessel will be considered, already characterized by LNG propulsion solution. Number, typology, size and integration on board of the generation units will be defined in relation with aspects of zonal independence, electrical load, weights, volumes, fuel tanks, supply systems, auxiliaries, with the minimum possible impact on commercially valuable space. In this perspective, fuel cells technology will be particularly taken into account. The critical issues in relation with the present safety rules and the whole ship design process will be addressed as a fundamental aspect

Innovative Energy Systems: Motivations, Challenges and Possible Solutions in the Cruise Ship Arena

The worldwide effort on the environmental issue in the maritime field has led to always more stringent regulations on greenhouse gas emission (GHG). In this perspective, the International Maritime Organization has developed regulations intended to increase the ship\u2019s efficiency and reduce GHG emissions both in design phase, through the introduction of an Energy Efficiency Design Index (EEDI), either in management phase, adopting the Ship Energy Efficiency Management Plan (SEEMP). In this challenging perspective, several approaches and technologies adopted in land-based engineering can also be advantageous for marine applications. This is the case of the Distributed Energy Resources (DER) solution applied in land-based microgrids, which increases both the system\u2019s efficiency and reliability. This work is primarily focused on methodological aspects related to the adoption of a DER solution on-board cruise ships, with the integration of different energy sources in order to pursue a more flexible, reliable and sustainable management of the ship. In this context, another engineering best practice developed for land-based applications that is further investigated in the paper is related to the on board thermal energy recovery issue, revisited due to the implementation of the DER solution

Value of Preoperative Inflammation-Based Prognostic Scores in Predicting Overall Survival and Disease-Free Survival in Patients with Gastric Cancer

This study was designed to identify which are the best preoperative inflammation-based prognostic scores in terms of overall survival (OS) and disease-free survival (DFS) in patients with gastric cancer.Between January 2004 and January 2013, 102 consecutive patients underwent resection for gastric cancer at S. Andrea Hospital, "La Sapienza", University of Rome. Their records were retrospectively reviewed.After a median follow up of 40.8 months (8-107 months), patients' 1-, 3-, and 5-year OS rates were 88, 72, and 59 %, respectively. After R0 resection, the 1-, 3-, and 5-year DFS rates were 93, 74, and 56 %, respectively. A multivariate analysis of the significant variables showed that only the modified Glasgow prognostic scores (p < 0.001) and PI (p < 0.001) were independently associated with OS. Regarding DFS, multivariate analysis of the significant variables showed that the modified Glasgow prognostic score (p = 0.002) and prognostic index (p < 0.001) were independently associated with DFS.The results of this study show that modified Glasgow prognostic score and prognostic index are independent predictors of OS and DFS in patients with gastric cancer