As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server.

Accurate localization is crucial for the autonomous operation of mobile robots. Specifically for indoor scenarios, localization algorithms typically rely on a previously generated map. However, many real-world sites like warehouses or healthcare environments violate the underlying assumption that the robot’s surroundings are mainly static. In this paper, we introduce a new dataset plus a benchmark that enables evaluating and comparing indoor localization methods in complex and changing real-world scenarios. While several datasets for indoor scenes exist, only a few combine the long-term localization aspect of repeatedly revisiting the same environment under varying conditions with precise ground truth over multiple rooms. Our dataset comprises various sequences recorded with a wheeled robot covering an office environment. We provide data from two 2D LiDARs, multiple consumer-grade RGB-D cameras, and the robot’s wheel odometry. By densely placing fiducial markers on every room ceiling, we can also provide accurate pose information within a single global frame for the whole environment, estimated through an additional upward-facing camera. We evaluate existing localization algorithms on our data and make the dataset together with a server-based benchmark evaluation publicly available. This facilitates an unbiased evaluation of localization approaches and enables further research on their application in challenging indoor scenarios.

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 0.6 s in average.

Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepancy between the map and the observed environment caused by such changes, we exploit human-readable localization cues to assist localization. These cues are readily available in most facilities and can be detected using RGB camera images by utilizing text spotting. We integrate these cues into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. By this, we provide a robust localization solution for environments with structural changes and dynamics by humans walking. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We release an open source implementation of our approach11https://github.com/PRBonn/tmcl, which uses off-the-shelf text spotting, written in C++ with a ROS wrapper.

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular mesh. We use the Poisson surface reconstruction to generate the mesh-based map representation. Based on the range images generated from the current LiDAR scan and the synthetic rendered views from the mesh-based map, we propose a new observation model and integrate it into a Monte Carlo localization framework, which achieves better localization performance and generalizes well to different environments. We test the proposed localization approach on multiple datasets collected in different environments with different LiDAR scanners. The experimental results show that our method can reliably and accurately localize a mobile system in different environments and operate online at the LiDAR sensor frame rate to track the vehicle pose.

Analyzing sensor data of plants and monitoring plant performance is a central element in different agricultural robotics applications. In plant science, phenotyping refers to analyzing plant traits for monitoring growth, for describing plant properties, or characterizing the plant's overall performance. It plays a critical role in the agricultural tasks and in plant breeding. Recently, there is a rising interest in using 3D data obtained from laser scanners and 3D cameras to develop automated non-intrusive techniques for estimating plant traits. In this paper, we address the problem of registering 3D point clouds of the plants over time, which is a backbone of applications interested in tracking spatio-temporal traits of individual plants. Registering plants over time is challenging due to its changing topology, anisotropic growth, and non-rigid motion in between scans. We propose a novel approach that exploits the skeletal structure of the plant and determines correspondences over time and drives the registration process. Our approach explicitly accounts for the non-rigidity and the growth of the plant over time in the registration. We tested our approach on a challenging dataset acquired over the course of two weeks and successfully registered the 3D plant point clouds recorded with a laser scanner forming a basis for developing systems for automated temporal plant-trait analysis.

Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-based metric with a magnitude-dependent scaling term. We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric. Our experimental evaluation indicate that our metric leads to more robust and more accurate estimates of the scene depth as well as camera trajectory. Thus, the metric improves camera pose estimation and in turn the mapping capabilities of mobile robots. We believe that a series of existing visual odometry and visual SLAM systems can benefit from the findings reported in this paper.

Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. The model predicts the overlap and yaw angle offset between the current sensor reading and virtual frames generated from a pre-built map. We integrate this observation model into a Monte-Carlo localization framework and tested it on urban datasets collected with a car in different seasons. The experiments presented in this paper illustrate that our method can reliably localize a vehicle in typical urban environments. We furthermore provide comparisons to a beam-endpoint and a histogram-based method indicating a superior global localization performance of our method with fewer particles.

Localization is a pre-requisite for most autonomous robots. For example, to carry out precision agriculture tasks effectively, a robot must be able to localize itself accurately in crop fields. The crop field environment presents unique challenges such as the highly repetitive structure of the crops leading to visual aliasing as well as the continuously changing appearance of the field, which makes it difficult to localize over time. In this paper, we present a localization system, which uses an aerial map of the field and exploits the semantic information of the crops, weeds, and their stem positions to resolve the visual ambiguity problem and to enable robot localization over extended periods of time. We evaluate our approach on a real field over multiple sessions spanning several weeks. Experiments suggest that our approach provides the necessary accuracy required by precision agriculture applications and works in cases where current techniques using typical visual features tend to fail.