Precision agriculture offers the opportunity to automate routine or difficult tasks in orchards and vineyards, such as spraying or inspection, with Uncrewed Ground Vehicles (UGV). In this context, human operators should be kept in the closed-loop control of the robot for safety and reliability. This work is motivated by the challenges of effectively deploying human-robot shared control in the field. First, an asymptotically stable controller keeps the robot on the desired trajectory between rows of trees, whose distance is on the order of the robot’s width. Second, the robot must efficiently avoid static and moving obstacles on its path. Third, the control inputs must not exceed the actuator limits, which can degrade trajectory tracking performance, cause instability, or damage critical hardware. Finally, in real-life scenarios, user intervention is sometimes required to manage unpredictable situations. To overcome these challenges, we propose and deploy a shared controller that continuously and smoothly varies the ratio of human and automatic control inputs depending on the human’s intent, geometrically rescales trajectory inputs to maintain bounded control, and incorporates obstacle avoidance capabilities – all while preserving asymptotic stability of the closed-loop system. Additionally, we introduce a time re-scaling strategy that modifies trajectory evolution, ensuring target positions remain within a defined vicinity of the robot. The system performance was assessed in simulation and in 26 field trials inside an apple orchard using different obstacle configurations, weather, and terrain conditions, with a success rate of 100% and an average tracking error of 0.1 m.Note to Practitioners—The proposed shared control approach is developed for use with a differentially steered Uncrewed Ground Vehicle (UGV) with first order kinematic constraints and can be adapted to different indoor and outdoor scenarios. The environment in which the UGV is deployed should be mapped in advance to create a reference trajectory for the UGV to follow. If the location of obstacles is not known in advance, an obstacle detection and tracking system, as well as an online mapping system, must be developed. A simulated model of the UGV and the environment are useful for determining initial values of the shared control gains that can be further tuned when the physical platform is first deployed. In GPS-denied scenarios, a Simultaneous Localization and Mapping (SLAM) system must be implemented; a lidar-inertial based SLAM system is recommended. A force-reflexive joystick is ideal for sensitive human input. In addition, the communication between the UGV and the base station (where the human operator supervises the UGV and provides commands to it) should have minimal time delays, not exceeding typical human reaction time (ca. 0.25 s).

Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data-despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1–2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar in a factor graph.

We present visual inertial lidar legged navigation system (VILENS), an odometry system for legged robots based on factor graphs. The key novelty is the tight fusion of four different sensor modalities to achieve reliable operation when the individual sensors would otherwise produce degenerate estimation. To minimize leg odometry drift, we extend the robot's state with a linear velocity bias term, which is estimated online. This bias is observable because of the tight fusion of this preintegrated velocity factor with vision, lidar, and inertial measurement unit (IMU) factors. Extensive experimental validation on different ANYmal quadruped robots is presented, for a total duration of 2 h and 1.8 km traveled. The experiments involved dynamic locomotion over loose rocks, slopes, and mud, which caused challenges such as slippage and terrain deformation. Perceptual challenges included dark and dusty underground caverns, and open and feature-deprived areas. We show an average improvement of 62% translational and 51% rotational errors compared to a state-of-the-art loosely coupled approach. To demonstrate its robustness, VILENS was also integrated with a perceptive controller and a local path planner.

Haptic sensing has recently been used effectively for legged robot localization in extreme scenarios where cam-eras and LiDAR might fail, such as dusty mines and foggy sewers. However, existing haptic sensing mainly relies on supervised classification, with training and evaluation executed over explicit terrain classes. Defining classes is a significant limitation to real-world applications, where prior labelling and handcrafted classes are often impractical. This paper proposes a novel haptic localization system based on a fully unsupervised terrain representation learned solely from the force/torque sensors located in the quadruped robot's feet. Instead of using the detected terrain class for localization, we propose an improved autoencoder architecture to generate a sparse map of encodings on the first run and to localize against this sparse map during subsequent runs. We compare our approach to a haptic localization system based on supervised terrain classification, showing that the unsupervised method has comparable or better performance than the supervised one for the same trajectories while clearly outperforming the proprioceptive odometry estimator available on the robot. Therefore, the proposed approach is well-suited for a routine maintenance application, increasing the platform's robustness.

In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera Performance Models (CPM) which evaluate the camera streams from the teach step to determine the most informative one for localization during the repeat step. By actively selecting the most suitable camera for localization, we are able to successfully complete missions when one of the cameras is occluded, faces into feature poor locations or if the environment has changed. Furthermore, we explore the specific challenges of achieving VT&R on a dynamic quadruped robot, ANYmal. The camera does not follow a linear path (due to the walking gait and holonomicity) such that precise path-following cannot be achieved. Our experiments feature forward and backward facing stereo cameras showing VT&R performance in cluttered indoor and outdoor scenarios. We compared the trajectories the robot executed during the repeat steps demonstrating typical tracking precision of less than 10 cm on average. With a view towards omni-directional localization, we show how the approach generalizes to four cameras in simulation.

We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum Li-DAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale reconstruction: integration of long-range LiDAR scans at high frequency, the capacity to deform the reconstruction after loop closures are detected, and scalability for long-duration exploration. Our system extends upon a state-of-the-art efficient RGB-D volumetric reconstruction technique, called supereight, to support LiDAR scans and a newly developed submapping technique to allow for dynamic correction of the 3D reconstruction. We then introduce a novel pose graph clustering and submap fusion feature to make the proposed system more scalable for large environments. We evaluate the performance using two public datasets including outdoor exploration with a handheld device and a drone, and with a mobile robot exploring an underground room network. Experimental results demonstrate that our system can reconstruct at 3 Hz with 60 m sensor range and ~5 cm resolution, while state-of-the-art approaches can only reconstruct to 25 cm resolution or 20 m range at the same frequency.

In this paper, we present a novel factor graph formulation to estimate the pose and velocity of a quadruped robot on slippery and deformable terrain. The factor graph introduces a preintegrated velocity factor that incorporates velocity inputs from leg odometry and also estimates related biases. From our experimentation we have seen that it is difficult to model uncertainties at the contact point such as slip or deforming terrain, as well as leg flexibility. To accommodate for these effects and to minimize leg odometry drift, we extend the robot's state vector with a bias term for this preintegrated velocity factor. The bias term can be accurately estimated thanks to the tight fusion of the preintegrated velocity factor with stereo vision and IMU factors, without which it would be unobservable. The system has been validated on several scenarios that involve dynamic motions of the ANYmal robot on loose rocks, slopes and muddy ground. We demonstrate a 26% improvement of relative pose error compared to our previous work and 52% compared to a state-of-the-art proprioceptive state estimator.

Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localization which exploits the foot contacts made by a quadruped robot to localize against a prior map of an environment, without the help of any camera or LIDAR sensor. The proposed method enables the robot to accurately re-localize itself after making a sequence of contact events over a terrain feature. The method is based on Sequential Monte Carlo and can support both 2.5D and 3D prior map representations. We have tested the approach online and onboard the ANYmal quadruped robot in two different scenarios: the traversal of a custom built wooden terrain course and a wall probing and following task. In both scenarios, the robot is able to effectively achieve a localization match and to execute a desired pre-planned path. The method keeps the localization error down to 10 cm on feature rich terrain by only using its feet, kinematic and inertial sensing.

In this paper, we present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km around New College, Oxford as well as a series of supplementary datasets with much more aggressive motion and lighting contrast. The datasets include data from two commercially available devices - a stereoscopic-inertial camera and a multi-beam 3D LiDAR, which also provides inertial measurements. Additionally, we used a tripod-mounted survey grade LiDAR scanner to capture a detailed millimeter-accurate 3D map of the test location (containing ~290 million points). Using the map, we generated a 6 Degrees of Freedom (DoF) ground truth pose for each LiDAR scan (with approximately 3 cm accuracy) to enable better benchmarking of LiDAR and vision localisation, mapping and reconstruction systems. This ground truth is the particular novel contribution of this dataset and we believe that it will enable systematic evaluation which many similar datasets have lacked. The large dataset combines both built environments, open spaces and vegetated areas so as to test localisation and mapping systems such as vision-based navigation, visual and LiDAR SLAM, 3D LiDAR reconstruction and appearance-based place recognition, while the supplementary datasets contain very dynamic motions to introduce more challenges for visual-inertial odometry systems. The datasets are available at:ori.ox.ac.uk/datasets/newer-college-dataset.

Agile robot locomotion on rough terrain is highly dependent on the ability to perceive the environment. In this paper, we show how the interaction between a reactive control framework and an online mapping system can significantly improve the trotting performance on irregular terrain. In particular, this new locomotion controller increases the stability of the robot and reduces frontal leg and shin collisions with obstacles by correcting in realtime the foothold locations. The mapping system uses an RGB-D sensor and a motion capture system to build a three dimensional map of the surroundings of the robot. While the robot is trotting, the control framework requests in advance a local heightmap around the next nominal foothold position. Then, an optimized foot placement location is estimated by applying visual pattern classification on the acquired heightmaps, and the leg endpoint trajectory is modified accordingly. The foothold correction is performed independently for each leg. To show the effectiveness of our approach the controller was tested both in simulation and experimentally with our 80 kg hydraulic quadruped robot, HyQ. The results show that visual based reaction through pattern classification is a promising approach to increase locomotion robustness over challenging terrain.