1.2k citations · 2.4k across the 26 of their papers we have counts for
27 papers
Real-time Event-camera Stereo Visual Odometry via Keytime Gaussian Process Regression
Nikan Nobari, Jonathan D. Gammell
Event cameras have microsecond-level temporal resolution and high dynamic range which make them more resilient to motion blur and poor illumination than standard frame-based camera…
: -Optimal Graph Search for Minimum Expected-Detection-Time Paths with Path Budget Constraints for Search and Rescue (SAR)
Eric Mugford, Jonathan D. Gammell
Searches are conducted to find missing persons and/or objects given uncertain information, imperfect observers and large search areas in Search and Rescue (SAR). In many scenarios,…
Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners
Mitchell E. C. Sabbadini, Andrew H. Liu, Joseph Ruan +3
Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the positi…
AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect
Tyler Wilson, Wil Thomason, Zachary Kingston +1
Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditiona…
Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
Tyler S. Wilson, Wil Thomason, Zachary Kingston +2
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. T…
Osprey: Multi-Session Autonomous Aerial Mapping with LiDAR-based SLAM and Next Best View Planning
Rowan Border, Nived Chebrolu, Yifu Tao +2
Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Aerial platforms that can fly GPS-guided preplanned m…
Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression
Jianeng Wang, Jonathan D. Gammell
Event-based cameras asynchronously capture individual visual changes in a scene. This makes them more robust than traditional frame-based cameras to highly dynamic motions and poor…
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Wil Thomason, Marlin P. Strub, Jonathan D. Gammell
High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms sol…
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
Rowan Border, Jonathan D. Gammell
High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of l…
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort
Valentin N. Hartmann, Marlin P. Strub, Marc Toussaint +1
Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across…
AIT* and EIT*: Asymmetric bidirectional sampling-based path planning
Marlin P. Strub, Jonathan D. Gammell
Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their searc…
Multimotion Visual Odometry (MVO)
Kevin M. Judd, Jonathan D. Gammell
Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These en…
Admissible heuristics for obstacle clearance optimization objectives
Marlin P. Strub, Jonathan D. Gammell
Obstacle clearance in state space is an important optimization objective in path planning because it can result in safe paths. This technical report presents admissible solution- a…
Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation
Rowan Border, Jonathan D. Gammell
The process of planning views to observe a scene is known as the Next Best View (NBV) problem. Approaches often aim to obtain high-quality scene observations while reducing the num…
Asymptotically Optimal Sampling-Based Motion Planning Methods
Jonathan D. Gammell, Marlin P. Strub
Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations…
Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
Marlin P. Strub, Jonathan D. Gammell
Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and u…
Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
Marlin P. Strub, Jonathan D. Gammell
Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approach…
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure
Kevin M. Judd, Jonathan D. Gammell
Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially chall…
The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth
Kevin M. Judd, Jonathan D. Gammell
Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in…
Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions
Kevin M. Judd, Jonathan D. Gammell, Paul Newman
Estimating motion from images is a well-studied problem in computer vision and robotics. Previous work has developed techniques to estimate the motion of a moving camera in a large…
Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations
Rowan Border, Jonathan D. Gammell, Paul Newman
Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model o…
Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search
Jonathan D. Gammell, Timothy D. Barfoot, Siddhartha S. Srinivasa
Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning al…
Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version)
Jonathan D Gammell, Timothy D Barfoot, Siddhartha S Srinivasa
Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find paths to every state in the search domain. This is inefficient once an initial solution is f…
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
Jonathan D. Gammell, Siddhartha S. Srinivasa, Timothy D. Barfoot
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples de…