10 research outputs found
A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots
Legged robots are becoming popular not only in research, but also in
industry, where they can demonstrate their superiority over wheeled machines in
a variety of applications. Either when acting as mobile manipulators or just as
all-terrain ground vehicles, these machines need to precisely track the desired
base and end-effector trajectories, perform Simultaneous Localization and
Mapping (SLAM), and move in challenging environments, all while keeping
balance. A crucial aspect for these tasks is that all onboard sensors must be
properly calibrated and synchronized to provide consistent signals for all the
software modules they feed. In this paper, we focus on the problem of
calibrating the relative pose between a set of cameras and the base link of a
quadruped robot. This pose is fundamental to successfully perform sensor
fusion, state estimation, mapping, and any other task requiring visual
feedback. To solve this problem, we propose an approach based on factor graphs
that jointly optimizes the mutual position of the cameras and the robot base
using kinematics and fiducial markers. We also quantitatively compare its
performance with other state-of-the-art methods on the hydraulic quadruped
robot HyQ. The proposed approach is simple, modular, and independent from
external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic
Computing (IEEE IRC 2019)
Present and Future of SLAM in Extreme Underground Environments
This paper reports on the state of the art in underground SLAM by discussing
different SLAM strategies and results across six teams that participated in the
three-year-long SubT competition. In particular, the paper has four main goals.
First, we review the algorithms, architectures, and systems adopted by the
teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to
approach for virtually all teams in the competition), heterogeneous multi-robot
operation (including both aerial and ground robots), and real-world underground
operation (from the presence of obscurants to the need to handle tight
computational constraints). We do not shy away from discussing the dirty
details behind the different SubT SLAM systems, which are often omitted from
technical papers. Second, we discuss the maturity of the field by highlighting
what is possible with the current SLAM systems and what we believe is within
reach with some good systems engineering. Third, we outline what we believe are
fundamental open problems, that are likely to require further research to break
through. Finally, we provide a list of open-source SLAM implementations and
datasets that have been produced during the SubT challenge and related efforts,
and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE
Transactions on Robotics for pre-approva
LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping
Lidar odometry has attracted considerable attention as a robust localization
method for autonomous robots operating in complex GNSS-denied environments.
However, achieving reliable and efficient performance on heterogeneous
platforms in large-scale environments remains an open challenge due to the
limitations of onboard computation and memory resources needed for autonomous
operation. In this work, we present LOCUS 2.0, a robust and
computationally-efficient \lidar odometry system for real-time underground 3D
mapping. LOCUS 2.0 includes a novel normals-based \morrell{Generalized
Iterative Closest Point (GICP)} formulation that reduces the computation time
of point cloud alignment, an adaptive voxel grid filter that maintains the
desired computation load regardless of the environment's geometry, and a
sliding-window map approach that bounds the memory consumption. The proposed
approach is shown to be suitable to be deployed on heterogeneous robotic
platforms involved in large-scale explorations under severe computation and
memory constraints. We demonstrate LOCUS 2.0, a key element of the CoSTAR
team's entry in the DARPA Subterranean Challenge, across various underground
scenarios.
We release LOCUS 2.0 as an open-source library and also release a
\lidar-based odometry dataset in challenging and large-scale underground
environments. The dataset features legged and wheeled platforms in multiple
environments including fog, dust, darkness, and geometrically degenerate
surroundings with a total of of operations and of distance
traveled
Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM
Multi-robot SLAM systems in GPS-denied environments require loop closures to
maintain a drift-free centralized map. With an increasing number of robots and
size of the environment, checking and computing the transformation for all the
loop closure candidates becomes computationally infeasible. In this work, we
describe a loop closure module that is able to prioritize which loop closures
to compute based on the underlying pose graph, the proximity to known beacons,
and the characteristics of the point clouds. We validate this system in the
context of the DARPA Subterranean Challenge and on numerous challenging
underground datasets and demonstrate the ability of this system to generate and
maintain a map with low error. We find that our proposed techniques are able to
select effective loop closures which results in 51% mean reduction in median
error when compared to an odometric solution and 75% mean reduction in median
error when compared to a baseline version of this system with no
prioritization. We also find our proposed system is able to find a lower error
in the mission time of one hour when compared to a system that processes every
possible loop closure in four and a half hours. The code and dataset for this
work can be found https://github.com/NeBula-Autonomy/LAM
LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments
Search and rescue with a team of heterogeneous mobile robots in unknown and
large-scale underground environments requires high-precision localization and
mapping. This crucial requirement is faced with many challenges in complex and
perceptually-degraded subterranean environments, as the onboard perception
system is required to operate in off-nominal conditions (poor visibility due to
darkness and dust, rugged and muddy terrain, and the presence of self-similar
and ambiguous scenes). In a disaster response scenario and in the absence of
prior information about the environment, robots must rely on noisy sensor data
and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of
the environment and localize themselves and potential survivors. To that end,
this paper reports on a multi-robot SLAM system developed by team CoSTAR in the
context of the DARPA Subterranean Challenge. We extend our previous work, LAMP,
by incorporating a single-robot front-end interface that is adaptable to
different odometry sources and lidar configurations, a scalable multi-robot
front-end to support inter- and intra-robot loop closure detection for large
scale environments and multi-robot teams, and a robust back-end equipped with
an outlier-resilient pose graph optimization based on Graduated Non-Convexity.
We provide a detailed ablation study on the multi-robot front-end and back-end,
and assess the overall system performance in challenging real-world datasets
collected across mines, power plants, and caves in the United States. We also
release our multi-robot back-end datasets (and the corresponding ground truth),
which can serve as challenging benchmarks for large-scale underground SLAM
Abiotic factors affecting the development of Ulva sp. (Ulvophyceae; Chlorophyta) in freshwater ecosystems
The influence of physicochemical factors on the development of Ulva species with distromatic tubular morphology was studied in three streams located in Poznan, Poland. The study evaluated key environmental factors that may influence the colonisation and growth of Ulva populations in freshwater systems. In total, nine environmental parameters were included: temperature, water depth, pH, oxygen (O2), ammonium (NH4),nitrate (NO3-), phosphate(PO43-), sodium chloride (NaCl) and total iron (Fe). Morphometric features of thalli (length and
width, percentage of furcated and young thalli) and surface area of free-floating mats formed by the freshwater populations of Ulva were compared at all sites. Principal components analysis indicated the
most important factors influencing Ulva development were sodium chloride concentrations and water depth. Two other key chemical factors affecting the freshwater form of Ulva were phosphate and nitrite concentrations. High concentrations of sodium chloride inhibited the development of Ulva, leading to a lower number of thalli in the Ulva mats. At the siteswith stable and deeper water, the surface area of the
mats was larger. Both phosphate and nitrite concentrations were positively correlated with an increase in the number of thalli in the mats and the thalli length