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In this paper, we present a modular and flexible state estimation framework for legged robots operating in real-world scenarios, where environmental conditions, such as occlusions, low light, rough terrain, and dynamic obstacles can severely impair estimation performance. At the core of the proposed estimation system, called Pronto, is an Extended Kalman Filter (EKF) that fuses IMU and Leg Odometry sensing for pose and velocity estimation. We also show how Pronto can integrate pose corrections from visual and LIDAR and odometry to correct pose drift in a loosely coupled manner. This allows it to have a real-time proprioceptive estimation thread running at high frequency (250–1,000 Hz) for use in the control loop while taking advantage of occasional (and often delayed) low frequency (1–15 Hz) updates from exteroceptive sources, such as cameras and LIDARs. To demonstrate the robustness and versatility of the approach, we have tested it on a variety of legged platforms, including two humanoid robots (the Boston Dynamics Atlas and NASA Valkyrie) and two dynamic quadruped robots (IIT HyQ and ANYbotics ANYmal) for more than 2 h of total runtime and 1.37 km of distance traveled. The tests were conducted in a number of different field scenarios under the conditions described above. The algorithms presented in this paper are made available to the research community as open-source ROS packages.

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments.

In this paper we investigate the application of semi-dense visual Simultaneous Localisation and Mapping (SLAM) to the humanoid robotics domain. Challenges of visual SLAM applied to humanoids include the type of dynamic motion executed by the robot, a lack of features in man-made environments and the presence of dynamics in the scene. Previous research on humanoid SLAM focused mostly on feature-based methods which result in sparse environment reconstructions. Instead, we investigate the application of a modern direct method to obtain a semi-dense visually interpretable map which can be used for collision free motion planning. We tackle the challenge of using direct visual SLAM on a humanoid by proposing a more robust pose tracking method. This isformulated as an optimisation problem over a cost function which combines information from the stereo camera and a lowdrift kinematic-inertial motion prior. Extensive experimental demonstrations characterise the performance of our method using the NASA Valkyrie humanoid robot in a laboratory environment equipped with a Vicon motion capture system. Our experiments demonstrate pose tracking robustness to challenges such as sudden view change, motion blur in the image, change in illumination and tracking through sequences of featureless areas in the environment. Finally, we provide a qualitative evaluation of our stereo reconstruction against a LIDAR map.

The variation in the antioxidant content (lycopene, β-carotene, ascorbic acid and total phenolics) was evaluated on two tomato genotypes during vine and post-harvest ripening. Tomatoes were sampled and analysed at seven ripening stages according to the colour value. The data indicate that ripening conditions affected both the antioxidant accumulation kinetics and the final content, which was higher in post-harvest-ripened fruits. In particular, lycopene mainly accumulated in the very last period of ripening and its content was not linearly related to colour changes. Antioxidant accumulation and other ripening indexes were not significantly different in the two tomato genotypes. © 1999 Society of Chemical Industry

In this paper we present a system for the state estimation of a dynamically walking and trotting quadruped. The approach fuses four heterogeneous sensor sources (inertial, kinematic, stereo vision and LIDAR) to maintain an accurate and consistent estimate of the robot’s base link velocity and position in the presence of disturbances such as slips and missteps. We demonstrate the performance of our system, which is robust to changes in the structure and lighting of the environment, as well as the terrain over which the robot crosses. Our approach builds upon a modular inertial-driven Extended Kalman Filter which incorporates a rugged, probabilistic leg odometry component with additional inputs from stereo visual odometry and LIDAR registration. The simultaneous use of both stereo vision and LIDAR helps combat operational issues which occur in real applications. To the best of our knowledge, this paper is the first to discuss the complexity of consistent estimation of pose and velocity states, as well as the fusion of multiple exteroceptive signal sources at largely different frequencies and latencies, in a manner which is acceptable for a quadruped’s feedback controller. A substantial experimental evaluation demonstrates the robustness and accuracy of our system, achieving continuously accurate localization and drift per distance traveled below 1 cm/m.