538 research outputs found
Learning to represent surroundings, anticipate motion and take informed actions in unstructured environments
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic
DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory
Occupancy mapping has been widely utilized to represent the surroundings for
autonomous robots to perform tasks such as navigation and manipulation. While
occupancy mapping in 2-D environments has been well-studied, there have been
few approaches suitable for 3-D dynamic occupancy mapping which is essential
for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping
algorithm called DSK3DOM. We first establish a Bayesian method to sequentially
update occupancy maps for a stream of measurements based on the random finite
set theory. Then, we approximate it with particles in the Dempster-Shafer
domain to enable real time computation. Moreover, the algorithm applies kernel
based inference with Dirichlet basic belief assignment to enable dense mapping
from sparse measurements. The efficacy of the proposed algorithm is
demonstrated through simulations and real experiments.Comment: 7 pages, 2 figures, submitted to ICRA 202
Uni-Fusion: Universal Continuous Mapping
We present Uni-Fusion, a universal continuous mapping framework for surfaces,
surface properties (color, infrared, etc.) and more (latent features in CLIP
embedding space, etc.). We propose the first universal implicit encoding model
that supports encoding of both geometry and different types of properties (RGB,
infrared, features, etc.) without requiring any training. Based on this, our
framework divides the point cloud into regular grid voxels and generates a
latent feature in each voxel to form a Latent Implicit Map (LIM) for geometries
and arbitrary properties. Then, by fusing a local LIM frame-wisely into a
global LIM, an incremental reconstruction is achieved. Encoded with
corresponding types of data, our Latent Implicit Map is capable of generating
continuous surfaces, surface property fields, surface feature fields, and all
other possible options. To demonstrate the capabilities of our model, we
implement three applications: (1) incremental reconstruction for surfaces and
color (2) 2D-to-3D transfer of fabricated properties (3) open-vocabulary scene
understanding by creating a text CLIP feature field on surfaces. We evaluate
Uni-Fusion by comparing it in corresponding applications, from which Uni-Fusion
shows high-flexibility in various applications while performing best or being
competitive. The project page of Uni-Fusion is available at
https://jarrome.github.io/Uni-Fusion/ .Comment: Published on IEEE Transactions on Robotics. Project page:
https://jarrome.github.io/Uni-Fusion
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