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This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz., Comment: Published in Transactions on Robotics. Edited 2022-11-07: Updated affiliation and citation

This article studies group-wise point set registration and makes the following contributions: "FuzzyGReg", which is a new fuzzy cluster-based method to register multiple point sets jointly, and "FuzzyQA", which is the associated quality assessment to check registration accuracy automatically. Given a group of point sets, FuzzyGReg creates a model of fuzzy clusters and equally treats all the point sets as the elements of the fuzzy clusters. Then, the group-wise registration is turned into a fuzzy clustering problem. To resolve this problem, FuzzyGReg applies a fuzzy clustering algorithm to identify the parameters of the fuzzy clusters while jointly transforming all the point sets to achieve an alignment. Next, based on the identified fuzzy clusters, FuzzyQA calculates the spatial properties of the transformed point sets and then checks the alignment accuracy by comparing the similarity degrees of the spatial properties of the point sets. When a local misalignment is detected, a local re-alignment is performed to improve accuracy. The proposed method is cost-efficient and convenient to be implemented. In addition, it provides reliable quality assessments in the absence of ground truth and user intervention. In the experiments, different point sets are used to test the proposed method and make comparisons with state-of-the-art registration techniques. The experimental results demonstrate the effectiveness of our method.The code is available at https://gitsvn-nt.oru.se/qianfang.liao/FuzzyGRegWithQA

Traditional approaches to manipulating the state of deformable linear objects (DLOs) - i.e., cables, ropes - rely on model-based planning. However, constructing an accurate dynamic model of a DLO is challenging due to the complexity of interactions and a high number of degrees of freedom. This renders the task of achieving a desired DLO shape particularly difficult and motivates the use of model-free alternatives, which while maintaining generality suffer from a high sample complexity. In this paper, we bridge the gap between these fundamentally different approaches and propose a framework that learns dynamic models of DLOs through trial-and-error interaction. Akin to model-based reinforcement learning (RL), we interleave learning and exploration to solve a 3D shape control task for a DLO. Our approach requires only a fraction of the interaction samples of the current state-of-the-art model-free RL alternatives to achieve superior shape control performance. Unlike offline model learning, our approach does not require expert knowledge for data collection, retains the ability to explore, and automatically selects relevant experience.

In this paper, we present a synopsis of the PlayGround project. Through neural-symbolic learning and reasoning, the PlayGround project assumes that high-level concepts and reasoning processes can be used to advance both symbol grounding and object affordance inference. However, a prerequisite for reasoning about objects and their affordances is integrated object representations that concurrently maintain symbolic values (e.g., high-level concepts), and sub-symbolic features (e.g., spatial aspects of objects). Integrated representations that, preferably, should be based upon neural-symbolic computation such that neural-symbolic models can, subsequently, be used for high-level reasoning processes. Nevertheless, reasoning processes for symbol grounding and affordance inference often require multiple inference steps. Taking inspiration from the cognitive prospects in simulation semantics, the PlayGround project further presumes that these reasoning processes can be simulated by neural rendering complementary to high-level reasoning processes.

Tracking deformable linear objects (DLOs) is a key element for applications where robots manipulate DLOs. However, the lack of distinctive features or appearance on the DLO and the object’s high-dimensional state space make tracking challenging and still an open question in robotics. In this paper, we propose a method for tracking the state of a DLO by applying a particle filter approach, where the posterior probability of each sample is estimated by a learned predictor. Our method can achieve accurate tracking even with no prerequisite segmentation which many related works require. Due to the differentiability of the posterior probability predictor, our method can leverage the gradients of posterior probabilities with respect to the latent states to improve the motion model in the particle filter. The preliminary experiments suggest that the proposed method can provide robust tracking results and the estimated DLO state converges quickly to the true state if the initial state is unknown.

In this paper we present the usage of PointNet, a deep neural network that consumes raw un-ordered point clouds, for detection of grape vine clusters in outdoor conditions. We investigate the added value of feeding the detection network with both RGB and depth, contradictory to common practice in agricultural robotics of relying on RGB only. A total of 5057 pointclouds (1033 manually annotated and 4024 annotated using geometric reasoning) were collected in a field experiment conducted in outdoor conditions on 9 grape vines and 5 plants. The detection results show overall accuracy of 91% (average class accuracy of 74%, precision 53% recall 48%) for RGBXYZ data and a significant drop in recall for RGB or XYZ data only. These results suggest the usage of depth cameras for vision in agricultural robotics is crucial for crops where the color contrast between the crop and the background is complex. The results also suggest geometric reasoning can be used for increased training set size, a major bottleneck in the development of agricultural vision systems.

An autonomous sweet pepper harvesting robot must perform several tasks to successfully harvest a fruit. Due to the highly unstructured environment in which the robot operates and the presence of occlusions, the current challenges are to improve the detection rate and lower the risk of losing sight of the fruit while approaching the fruit for harvest. Therefore, it is crucial to choose the best approach direction with least occlusion from obstacles. The value of ideal information regarding the best approach direction was evaluated by comparing it to a method attempting several directions until successful harvesting is performed. A laboratory experiment was conducted on artificial sweet pepper plants using a system based on eye-in-hand configuration comprising a 6DOF robotic manipulator equipped with an RGB camera. The performance is evaluated in laboratorial conditions using both descriptive statistics of the average harvesting times and harvesting success as well as regression models. The results show roughly 40–45% increase in average harvest time when no a-priori information of the correct harvesting direction is available with a nearly linear increase in overall harvesting time for each failed harvesting attempt. The variability of the harvesting times grows with the number of approaches required, causing lower ability to predict them. Tests show that occlusion of the front of the peppers significantly impacts the harvesting times. The major reason for this is the limited workspace of the robot often making the paths to positions to the side of the peppers significantly longer than to positions in front of the fruit which is more open.

Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties - assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper postures. We propose a fast method for extracting grasp envelopes for objects that fit within a known shape category, placed in an unknown environment. Our approach is based on grasp envelope primitives, which encode knowledge of human grasping strategies. We use environment models, reconstructed from noisy sensor observations, to refine the grasp envelope primitives and extract bounded envelopes of collision-free gripper postures. Also, we evaluate the envelope extraction procedure both in a stand alone fashion, as well as an integrated component of an autonomous picking system.

Mobile robotic telepresence (MRP) has been introduced to allow communication from remote locations. Modern MRP systems offer rich capabilities for human-human interactions. However, simply driving a telepresence robot can become a burden especially for novice users, leaving no room for interaction at all. In this video we introduce a project which aims to incorporate advanced robotic algorithms into manned telepresence robots in a natural way to allow human-robot cooperation for safe driving. It also shows a very first implementation of cooperative driving based on extracting a safe drivable area in real time using the image stream received from the robot. Categories and Subject Descriptors I.2.9 [Robotics]: Commercial robots and applications; H.5.2 [User interfaces]: Graphical user interfaces (GUI)

The transfer of research results to market-ready products is often a costly and time-consuming process. In order to generate successful products, researchers must cooperate with industrial companies; both the industrial and academic partners need to have a detailed understanding of the requirements of all parties concerned. Academic researchers need to identify the performance indicators for technical systems within a business environment and be able to apply them. Inservice logistics today, nearly all standardized mass goods are unloaded manually with one reason for this being the undefined position and orientation of the goods in the carrier. A study regarding the qualitative and quantitative properties of goods that are transported in containers shows that there is a huge economic relevance for autonomous systems. In 2008, more than 8,4 billion Twenty-foot equivalent units (TEU) were imported and unloaded manually at European ports, corresponding to more than 331,000 billion single goods items. Besides the economic relevance, the opinion of market participants is an important factor for the success of new systems on the market. The main outcomes of a study regarding the challenges, opportunities and barriers in robotic-logistics, allow for the estimation of the economic efficiency of performance indicators, performance flexibility and soft factors. The economic efficiency of the performance parameters is applied to the parcel robot – a cognitive system to unload parcels autonomously from containers. In the following article, the results of the study are presented and the resultant conclusions discussed.