351 research outputs found
WSR: A WiFi Sensor for Collaborative Robotics
In this paper we derive a new capability for robots to measure relative
direction, or Angle-of-Arrival (AOA), to other robots operating in
non-line-of-sight and unmapped environments with occlusions, without requiring
external infrastructure. We do so by capturing all of the paths that a WiFi
signal traverses as it travels from a transmitting to a receiving robot, which
we term an AOA profile. The key intuition is to "emulate antenna arrays in the
air" as the robots move in 3D space, a method akin to Synthetic Aperture Radar
(SAR). The main contributions include development of i) a framework to
accommodate arbitrary 3D trajectories, as well as continuous mobility all
robots, while computing AOA profiles and ii) an accompanying analysis that
provides a lower bound on variance of AOA estimation as a function of robot
trajectory geometry based on the Cramer Rao Bound. This is a critical
distinction with previous work on SAR that restricts robot mobility to
prescribed motion patterns, does not generalize to 3D space, and/or requires
transmitting robots to be static during data acquisition periods. Our method
results in more accurate AOA profiles and thus better AOA estimation, and
formally characterizes this observation as the informativeness of the
trajectory; a computable quantity for which we derive a closed form. All
theoretical developments are substantiated by extensive simulation and hardware
experiments. We also show that our formulation can be used with an
off-the-shelf trajectory estimation sensor. Finally, we demonstrate the
performance of our system on a multi-robot dynamic rendezvous task.Comment: 28 pages, 25 figures, *co-primary author
DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
To achieve collaborative tasks, robots in a team need to have a shared
understanding of the environment and their location within it. Distributed
Simultaneous Localization and Mapping (SLAM) offers a practical solution to
localize the robots without relying on an external positioning system (e.g.
GPS) and with minimal information exchange. Unfortunately, current distributed
SLAM systems are vulnerable to perception outliers and therefore tend to use
very conservative parameters for inter-robot place recognition. However, being
too conservative comes at the cost of rejecting many valid loop closure
candidates, which results in less accurate trajectory estimates. This paper
introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection
mechanism that can work with less conservative parameters. DOOR-SLAM is based
on peer-to-peer communication and does not require full connectivity among the
robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined
with a distributed pairwise consistent measurement set maximization algorithm
to reject spurious inter-robot loop closures; and a distributed SLAM front-end
that detects inter-robot loop closures without exchanging raw sensor data. The
system has been evaluated in simulations, benchmarking datasets, and field
experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM
produces more inter-robot loop closures, successfully rejects outliers, and
results in accurate trajectory estimates, while requiring low communication
bandwidth. Full source code is available at
https://github.com/MISTLab/DOOR-SLAM.git.Comment: 8 pages, 11 figures, 2 table
DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems
An optimization problem is at the heart of many robotics estimating,
planning, and optimum control problems. Several attempts have been made at
model-based multi-robot localization, and few have formulated the multi-robot
collaborative localization problem as a factor graph problem to solve through
graph optimization. Here, the optimization objective is to minimize the errors
of estimating the relative location estimates in a distributed manner. Our
novel graph-theoretic approach to solving this problem consists of three major
components; (connectivity) graph formation, expansion through transition model,
and optimization of relative poses. First, we estimate the relative
pose-connectivity graph using the received signal strength between the
connected robots, indicating relative ranges between them. Then, we apply a
motion model to formulate graph expansion and optimize them using go graph
optimization as a distributed solver over dynamic networks. Finally, we
theoretically analyze the algorithm and numerically validate its optimality and
performance through extensive simulations. The results demonstrate the
practicality of the proposed solution compared to a state-of-the-art algorithm
for collaborative localization in multi-robot systems.Comment: Preprint of the Paper Accepted to DARS 202
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
Compute-Bound and Low-Bandwidth Distributed 3D Graph-SLAM
This article describes a new approach for distributed 3D SLAM map building.
The key contribution of this article is the creation of a distributed
graph-SLAM map-building architecture responsive to bandwidth and computational
needs of the robotic platform. Responsiveness is afforded by the integration of
a 3D point cloud to plane cloud compression algorithm that approximates dense
3D point cloud using local planar patches. Compute bound platforms may restrict
the computational duration of the compression algorithm and low-bandwidth
platforms can restrict the size of the compression result. The backbone of the
approach is an ultra-fast adaptive 3D compression algorithm that transforms
swaths of 3D planar surface data into planar patches attributed with image
textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and
extends it by computationally isolating map integration tasks from local
Guidance, Navigation, and Control tasks and includes an addition of a network
protocol to share the compressed plane clouds. The joint effect of these
contributions allows agents with 3D sensing capabilities to calculate and
communicate compressed map information commensurate with their onboard
computational resources and communication channel capacities. This opens SLAM
mapping to new categories of robotic platforms that may have computational and
memory limits that prohibit other SLAM solutions
NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology,
under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and
Defense Advanced Research Projects Agency (DARPA)
Satellite Articulation Sensing using Computer Vision
Autonomous on-orbit satellite servicing benefits from an inspector satellite that can gain as much information as possible about the primary satellite. This includes performance of articulated objects such as solar arrays, antennas, and sensors. A method for building an articulated model from monocular imagery using tracked feature points and the known relative inspection route is developed. Two methods are also developed for tracking the articulation of a satellite in real-time given an articulated model using both tracked feature points and image silhouettes. Performance is evaluated for multiple inspection routes and the effect of inspection route noise is assessed. Additionally, a satellite model is built and used to collect stop-motion images simulating articulated motion over an inspection route under simulated space illumination. The images are used in the silhouette articulation tracking method and successful tracking is demonstrated qualitatively. Finally, a human pose tracking algorithm is modified for tracking the satellite articulation demonstrating the applicability of human tracking methods to satellite articulation tracking methods when an articulated model is available
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