22 accepted papers
Factor graph chains– the special case of a factor graph in which there are no potentials connecting non-adjacent nodes– arise naturally in many robotics problems. Importantly, they are often part of an inner loop in trajectory optimization and estimation problems, and so applications can be very sen…
In this paper, we propose a flexible mapping scheme that uses a masking function (mask) to focus the attention of a pose graph SLAM (Simultaneous Localization and Mapping) system. The masking function takes the robot's observations and returns true if the robot is in an important location. State-of-…
Fiducials are artificial features with a variety of uses in computer vision such as object tracking and localization. We propose the idea of flexible tag layouts for visual fiducial systems. In contrast to traditional square tags, flexible tag layouts allow circular, annular, or other shapes as desi…
In this paper, we describe a method for global localization in a previously unvisited environment using only a schematic floor plan as a prior map. The floor plan need not be a precision map-it can be the sort of image found in buildings to guide people or aid evacuation. The core idea is to identif…
We propose an ultra-wideband-based (UWB) localization system that achieves high accuracy through nonparametric estimation of measurement probability densities and explicit modeling of antenna delays. This problem is difficult because non-line-of-sight conditions give rise to multimodal errors, which…
For online robots, incremental SLAM algorithms offer huge potential computational savings over batch algorithms. The dominant incremental algorithms are iSAM and iSAM2 which offer radically different approaches to computing incremental updates, balancing issues like 1) the need to re-linearize, 2) c…
In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these i…
Risk-aware Multi-Policy Decision Making (MPDM)is a powerful framework for reliable navigation in a dynamic social environment where rather than evaluating individual trajectories, a “library” of policies (reactive controllers)is evaluated by anticipating potentially dangerous future outcomes using a…
In GPS-denied environments, robot systems typically revert to navigating with dead-reckoning and relative mapping, accumulating error in their global pose estimate. In this paper, we propose Feature-based Localization between Air and Ground (FLAG), a method for computing global position updates by m…
In the Multi-Policy Decision Making (MPDM) framework, a robot's policy is elected by sampling from the distribution of current states, predicting future outcomes through forward simulation, and selecting the policy with the best expected performance. Electing the best plan depends on sampling initia…
Mobile robotic teams require robust communication in order to coordinate effectively, which is a challenge given the dynamic, unpredictable nature of mobile ad hoc networks (MANET). These networks are subject to rapidly varying link qualities as robots move through their environment. Improving the r
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a fa…
In dynamic environments crowded with people, robot motion planning becomes difficult due to the complex and tightly-coupled interactions between agents. Trajectory planning methods, supported by models of typical human behavior and personal space, often produce reasonable behavior. However, they do…
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface…
Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a feature-bas…
The goal of this paper is to develop a robot with a grounded spatial vocabulary. Such a vocabulary would allow it to give and follow directions, and would give it valuable additional information in aiding localization and navigation. We approach the problem by defining an ontology of space (includin…
This paper reports on an algorithm to support autonomous vehicles in reasoning about occluded regions of their environment to make safe, reliable decisions. In autonomous driving scenarios, other traffic participants are often occluded from sensor measurements by buildings or large vehicles like bus…
We describe a new multi-resolution scan matching method that makes exhaustive (and thus local-minimum-proof) matching practical, even for large positional uncertainties. Unlike earlier multi-resolution methods, in which putative matches at low-resolutions can lead the matcher to an incorrect solutio…
Real-world autonomous driving in city traffic must cope with dynamic environments including other agents with uncertain intentions. This poses a challenging decision-making problem, e.g., deciding when to perform a passing maneuver or how to safely merge into traffic. Previous work in the literature…
We consider the problem of a human-following robot in which a human is equipped with a low-fidelity odometry sensor and a robot follows the human leader - often lagging well behind and out of visual contact with the human. The challenge is for the robot to determine the path taken by the human, desp…
To operate reliably in real-world traffic, an autonomous car must evaluate the consequences of its potential actions by anticipating the uncertain intentions of other traffic participants. This paper presents an integrated behavioral inference and decision-making approach that models vehicle behavio…
Image feature descriptors composed of a series of binary intensity comparisons yield substantial memory and runtime improvements over conventional descriptors, but are sensitive to viewpoint changes in ways that vary per feature. We propose a method to improve the matching performance of such descri…