675 research outputs found
Vision-based legged robot navigation: localisation, local planning, learning
The recent advances in legged locomotion control have made legged robots walk up staircases, go deep into underground caves, and walk in the forest. Nevertheless, autonomously achieving this task is still a challenge. Navigating and acomplishing missions in the wild relies not only on robust low-level controllers but also higher-level representations and perceptual systems that are aware of the robot's capabilities.
This thesis addresses the navigation problem for legged robots. The contributions are four systems designed to exploit unique characteristics of these platforms, from the sensing setup to their advanced mobility skills over different terrain. The systems address localisation, scene understanding, and local planning, and advance the capabilities of legged robots in challenging environments.
The first contribution tackles localisation with multi-camera setups available on legged platforms. It proposes a strategy to actively switch between the cameras and stay localised while operating in a visual teach and repeat context---in spite of transient changes in the environment. The second contribution focuses on local planning, effectively adding a safety layer for robot navigation. The approach uses a local map built on-the-fly to generate efficient vector field representations that enable fast and reactive navigation. The third contribution demonstrates how to improve local planning in natural environments by learning robot-specific traversability from demonstrations. The approach leverages classical and learning-based methods to enable online, onboard traversability learning. These systems are demonstrated via different robot deployments on industrial facilities, underground mines, and parklands.
The thesis concludes by presenting a real-world application: an autonomous forest inventory system with legged robots. This last contribution presents a mission planning system for autonomous surveying as well as a data analysis pipeline to extract forestry attributes. The approach was experimentally validated in a field campaign in Finland, evidencing the potential that legged platforms offer for future applications in the wild
Planning maximum-manipulability cutting paths
This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape but observes it from a noisy point cloud. We consider the multi-objective optimisation problem of finding robot trajectories which maximise the robot’s manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g., demolition of the legacy nuclear plant, where the cut path needs not be precise as long as it achieves dismantling. We show how detours in the path can be leveraged to increase the manipulability of the robot at all points along the path. This helps to avoid singularities while maximising the robot’s capability to make small deviations during task execution. We show how a sampling-based planner can be projected onto the Riemannian manifold of a curved surface, and extended to include a term which maximises manipulability. We present the results of empirical experiments, with both simulated and real robots, which are tasked with moving over a variety of different surface shapes. Our planner enables successful task completion while ensuring significantly greater manipulability when compared against a conventional RRT* planner
Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
Motivated by the tremendous progress we witnessed in recent years, this paper
presents a survey of the scientific literature on the topic of Collaborative
Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM.
With fleets of self-driving cars on the horizon and the rise of multi-robot
systems in industrial applications, we believe that Collaborative SLAM will
soon become a cornerstone of future robotic applications. In this survey, we
introduce the basic concepts of C-SLAM and present a thorough literature
review. We also outline the major challenges and limitations of C-SLAM in terms
of robustness, communication, and resource management. We conclude by exploring
the area's current trends and promising research avenues.Comment: 44 pages, 3 figure
GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration
Using geometric landmarks like lines and planes can increase navigation
accuracy and decrease map storage requirements compared to commonly-used LiDAR
point cloud maps. However, landmark-based registration for applications like
loop closure detection is challenging because a reliable initial guess is not
available. Global landmark matching has been investigated in the literature,
but these methods typically use ad hoc representations of 3D line and plane
landmarks that are not invariant to large viewpoint changes, resulting in
incorrect matches and high registration error. To address this issue, we adopt
the affine Grassmannian manifold to represent 3D lines and planes and prove
that the distance between two landmarks is invariant to rotation and
translation if a shift operation is performed before applying the Grassmannian
metric. This invariance property enables the use of our graph-based data
association framework for identifying landmark matches that can subsequently be
used for registration in the least-squares sense. Evaluated on a challenging
landmark matching and registration task using publicly-available LiDAR
datasets, our approach yields a 1.7x and 3.5x improvement in successful
registrations compared to methods that use viewpoint-dependent centroid and
"closest point" representations, respectively.Comment: accepted to RA-L; 8 pages. arXiv admin note: text overlap with
arXiv:2205.0855
- …