1,341 research outputs found
On Quantifying Qualitative Geospatial Data: A Probabilistic Approach
Living in the era of data deluge, we have witnessed a web content explosion,
largely due to the massive availability of User-Generated Content (UGC). In
this work, we specifically consider the problem of geospatial information
extraction and representation, where one can exploit diverse sources of
information (such as image and audio data, text data, etc), going beyond
traditional volunteered geographic information. Our ambition is to include
available narrative information in an effort to better explain geospatial
relationships: with spatial reasoning being a basic form of human cognition,
narratives expressing such experiences typically contain qualitative spatial
data, i.e., spatial objects and spatial relationships.
To this end, we formulate a quantitative approach for the representation of
qualitative spatial relations extracted from UGC in the form of texts. The
proposed method quantifies such relations based on multiple text observations.
Such observations provide distance and orientation features which are utilized
by a greedy Expectation Maximization-based (EM) algorithm to infer a
probability distribution over predefined spatial relationships; the latter
represent the quantified relationships under user-defined probabilistic
assumptions. We evaluate the applicability and quality of the proposed approach
using real UGC data originating from an actual travel blog text corpus. To
verify the quality of the result, we generate grid-based maps visualizing the
spatial extent of the various relations
Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference
This article presents a novel and flexible multitask multilayer Bayesian
mapping framework with readily extendable attribute layers. The proposed
framework goes beyond modern metric-semantic maps to provide even richer
environmental information for robots in a single mapping formalism while
exploiting intralayer and interlayer correlations. It removes the need for a
robot to access and process information from many separate maps when performing
a complex task, advancing the way robots interact with their environments. To
this end, we design a multitask deep neural network with attention mechanisms
as our front-end to provide heterogeneous observations for multiple map layers
simultaneously. Our back-end runs a scalable closed-form Bayesian inference
with only logarithmic time complexity. We apply the framework to build a dense
robotic map including metric-semantic occupancy and traversability layers.
Traversability ground truth labels are automatically generated from
exteroceptive sensory data in a self-supervised manner. We present extensive
experimental results on publicly available datasets and data collected by a 3D
bipedal robot platform and show reliable mapping performance in different
environments. Finally, we also discuss how the current framework can be
extended to incorporate more information such as friction, signal strength,
temperature, and physical quantity concentration using Gaussian map layers. The
software for reproducing the presented results or running on customized data is
made publicly available
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
In this paper, we develop a modular neural network for real-time semantic
mapping in uncertain environments, which explicitly updates per-voxel
probabilistic distributions within a neural network layer. Our approach
combines the reliability of classical probabilistic algorithms with the
performance and efficiency of modern neural networks. Although robotic
perception is often divided between modern differentiable methods and classical
explicit methods, a union of both is necessary for real-time and trustworthy
performance. We introduce a novel Convolutional Bayesian Kernel Inference
(ConvBKI) layer which incorporates semantic segmentation predictions online
into a 3D map through a depthwise convolution layer by leveraging conjugate
priors. We compare ConvBKI against state-of-the-art deep learning approaches
and probabilistic algorithms for mapping to evaluate reliability and
performance. We also create a Robot Operating System (ROS) package of ConvBKI
and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
Robotic perception is currently at a cross-roads between modern methods which
operate in an efficient latent space, and classical methods which are
mathematically founded and provide interpretable, trustworthy results. In this
paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer
which explicitly performs Bayesian inference within a depthwise separable
convolution layer to simultaneously maximize efficiency while maintaining
reliability. We apply our layer to the task of 3D semantic mapping, where we
learn semantic-geometric probability distributions for LiDAR sensor information
in real time. We evaluate our network against state-of-the-art semantic mapping
algorithms on the KITTI data set, and demonstrate improved latency with
comparable semantic results
CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization
In this paper, we consider improving the efficiency of information-based
autonomous robot exploration in unknown and complex environments. We first
utilize Gaussian process (GP) regression to learn a surrogate model to infer
the confidence-rich mutual information (CRMI) of querying control actions, then
adopt an objective function consisting of predicted CRMI values and prediction
uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO).
The trade-off between the best action with the highest CRMI value
(exploitation) and the action with high prediction variance (exploration) can
be realized. To further improve the efficiency of GPBO, we propose a novel
lightweight information gain inference method based on Bayesian kernel
inference and optimization (BKIO), achieving an approximate logarithmic
complexity without the need for training. BKIO can also infer the CRMI and
generate the best action using BO with bounded cumulative regret, which ensures
its comparable accuracy to GPBO with much higher efficiency. Extensive
numerical and real-world experiments show the desired efficiency of our
proposed methods without losing exploration performance in different
unstructured, cluttered environments. We also provide our open-source
implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.Comment: Full version for the paper accepted by IEEE Robotics and Automation
Letters (RA-L) 2023. arXiv admin note: text overlap with arXiv:2301.0052
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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