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Found 19 papers, 1 papers with code
Staircase Localization for Autonomous Exploration in Urban Environments
From the ROI, line segment features are extracted using a deep line segment detection algorithm.
Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments
In the proposed approach, information from the event camera and LiDAR are fused to localize a human or an object-of-interest in a robot's local frame.
FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses.
Taxonomy of A Decision Support System for Adaptive Experimental Design in Field Robotics
Experimental design in field robotics is an adaptive human-in-the-loop decision-making process in which an experimenter learns about system performance and limitations through interactions with a robot in the form of constructed experiments.
Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors.
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain
Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert.
Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory Control Interface
Real-world deployment of new technology and capabilities can be daunting.
Learning Risk-aware Costmaps for Traversability in Challenging Environments
One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move.
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution.
Robotics
DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments
Enabling fully autonomous robots capable of navigating and exploring large-scale, unknown and complex environments has been at the core of robotics research for several decades.
Loop Closure Detection
Simultaneous Localization and Mapping
Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages.
Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints
Monocular depth inference has gained tremendous attention from researchers in recent years and remains as a promising replacement for expensive time-of-flight sensors, but issues with scale acquisition and implementation overhead still plague these systems.
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments
In recent years, unsupervised deep learning approaches have received significant attention to estimate the depth and visual odometry (VO) from unlabelled monocular image sequences.
Confidence-rich grid mapping
Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments.
Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud
This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds.
Deep Learning Tubes for Tube MPC
This uncertainty may come from errors in learning (due to a lack of data, for example), or may be inherent to the system.
Bayesian Learning-Based Adaptive Control for Safety Critical Systems
We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties.
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP).
