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.

Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras, may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots that fuses the measurements from a body-mounted inertial measurement unit (Body-IMU), joint encoders, and multiple leg-mounted IMUs (Leg-IMU) using an extended Kalman filter (EKF). The filter system contains the error states of all IMU frames. The Leg-IMUs are used to detect foot contact, thereby providing zero-velocity measurements to update the state of the Leg-IMU frames. Additionally, we compute the relative position constraints between the Body-IMU and Leg-IMUs by the leg kinematics and use them to update the main body state and reduce the error drift of the individual IMU frames. Field experimental results have shown that our proposed DogLegs system achieves better state estimation accuracy compared to the traditional leg odometry method (using only Body-IMU and joint encoders) across various terrains. We make our datasets publicly available to benefit the research community (https://github.com/YibinWu/leg-odometry).

A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning system. However, it still drifts over extended periods because of the absence of external correction signals. To achieve the goal of long-term accurate localization, we propose Wheel-GINS, a Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation system using a Wheel-IMU. Wheel-GINS fuses the GNSS position measurement with the Wheel-IMU via an extended Kalman filter to limit the long-term error drift and provide continuous state estimation when the GNSS signal is blocked. Considering the specificities of the GNSS/Wheel-IMU integration, we conduct detailed modeling and online estimation of the Wheel-IMU installation parameters, including the Wheel-IMU leverarm and mounting angle and the wheel radius error. Experimental results have shown that Wheel-GINS outperforms the traditional GNSS/Odometer/INS integrated navigation system during GNSS outages. At the same time, Wheel-GINS can effectively estimate the Wheel-IMU installation parameters online and, consequently, improve the localization accuracy and practicality of the system. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-GINS).

The creation of precise and high-resolution crop point clouds in agricultural fields has become a key challenge for high-throughput phenotyping applications. This work implements a novel calibration method to calibrate the laser scanning system of an agricultural field robot consisting of two industrial-grade laser scanners used for high-precise 3D crop point cloud creation. The calibration method optimizes the transformation between the scanner origins and the robot pose by minimizing 3D point omnivariances within the point cloud. Moreover, we present a novel factor graph-based pose estimation method that fuses total station prism measurements with IMU and GNSS heading information for high-precise pose determination during calibration. The root-mean-square error of the distances to a georeferenced ground truth point cloud results in 0.8 cm after parameter optimization. Furthermore, our results show the importance of a reference point cloud in the calibration method needed to estimate the vertical translation of the calibration. Challenges arise due to non-static parameters while the robot moves, indicated by systematic deviations to a ground truth terrestrial laser scan.

Odometry estimation is crucial for every autonomous system requiring navigation in an unknown environment. In modern mobile robots, 3D LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and IMU measurements, these systems can reduce the accumulated drift caused by sequentially registering individual LiDAR scans and provide a robust pose estimate. Although effective, LiDAR-inertial odometry systems require proper parameter tuning to be deployed. In this paper, we propose LIO-EKF, a tightly-coupled LiDAR-inertial odometry system based on point-to-point registration and the classical extended Kalman filter scheme. We propose an adaptive data association that considers the relative pose uncertainty, the map discretization errors, and the LiDAR noise. In this way, we can substantially reduce the parameters to tune for a given type of environment. The experimental evaluation suggests that the proposed system performs on par with the state-of-the-art LiDAR-inertial odometry pipelines but is significantly faster in computing the odometry. The source code of our implementation is publicly available (https://github.com/YibinWu/LIO-EKF).

Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.

The work presents a foot-mounted sensor system for a combined indoor/outdoor pedestrian localization. The approach is based on a zero-velocity update scheme formulated as an Extended or Unscented Kalman filter with quaternion orientation representation and employs a custom low-cost sensor unit. Both filters are compared in terms of speed and accuracy on a representative trajectory. A detailed discussion is provided with respect to different filter state formulations, stance still detection mechanisms and associated filter parameters. The presented pure inertial system is augmented with magnetic field measurements for heading correction. The challenging localization scenario with an elevator is addressed by augmenting the system with a barometric pressure sensor for height error correction. The work also demonstrates how the basic algorithm version can be extended with reference systems such as GPS and passive RFID tags on the floor for absolute position drift correction.

The work presents a system for sensor data and complementary information fusion for localization in indoor environments. The system is based on modular sensor units, which can be attached to a person and contains various sensors, such as range sensors, inertial and magnetic sensors, a GPS receiver and a barometer. The measurements are processed using Bayesian Recursive Estimation algorithms and combined with available a priori knowledge such as map information or human motion models and constraints. The processing can be done locally, since all necessary data are available on the mobile unit. This system provides a platform for implementation, combination and evaluation of various localization principles and can be used for a variety of applications, such as indoor and outdoor pedestrian navigation, localization of other objects such as vehicles as well as robotics applications.

This paper describes the development and deployment of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is preprocessed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information. The performance of the network protocols and localisation algorithm are evaluated using simulated and real experimental data.

This paper describes our demonstration of a wireless sensor network for monitoring human motion and position in an indoor environment. Mobile sensor nodes comprising mote-type devices, along with inertial sensors are worn by persons moving inside buildings. Motion data is pre-processed onboard mobile nodes and transferred to a static network of seed nodes using a delay tolerant protocol with minimal radio packet overhead. A Monte Carlo based localisation algorithm is implemented, which uses a person's pedometry data, indoor map information and seed node positions to provide accurate, real-time indoor location information.

This short-paper presents the design and experimental validation of model-based, mobile routing protocol for a delay tolerant network (DTN), where herds of animals are utilised as message ferries. We develop a novel routing protocol that utilises knowledge of the predicted behaviour of each ferry in order to choose optimal ferries for carrying messages from source to sink nodes, as well as minimise routing overhead of the network via adaptive beaconing based on current behaviour.

We resent six degrees of freedom tracking system, which is wireless and scalable concerning the tracking volume and the number of devices being tracked. This is achieved by the modular design of the system consisting of two different types of modules: an arbitrary number of tracked user modules and a number of fixed reference modules. It provides a flexible setup for head tracking in virtual and augmented reality environments and for various other applications such as motion capture and analysis. The system combines inertial sensor data with ultrasonic ranging measurements to determine orientation and absolute position.