Online Continuous Mapping using Gaussian Process Implicit Surfaces

Lee, Bhoram; Zhang, Clark; Huang, Zonghao; Lee, Daniel D.

doi:10.1109/icra.2019.8794324

Cited by 45 publications

(77 citation statements)

References 22 publications

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“…In our previous work [16], the inconsistency in discrete space was dealt with by using Markov random fields to regularise the grid. The static mapping methods [17][18][19][20][21][22][23], based on Gaussian Random Fields (GRFs), build smooth occupancy grid maps and predict the occupancy of unobserved space in continuous space. This paper is motivated by them and proposes a new dynamic mapping method based on GRF that is able to deal with continuous space instead of a discrete grid.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

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HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments.

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Section: Research Gap and Motivationmentioning

confidence: 99%

“…A nested Bayesian committee machine is proposed to learn online 3D occupancy maps using Gaussian processes [22]. Online continuous mapping is proposed to build a map as the zero level set of a Gaussian process implicit surface [23].…”

Section: Related Workmentioning

confidence: 99%

HMM-Based Dynamic Mapping with Gaussian Random Fields

Barão

Rato

et al. 2022

Electronics

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“…However, the normal GP formulation is limited to 2D terrains or 3D Euclidean space. To facilitate mapping 3D surfaces, Gaussian process implicit surfaces (GPISs) [23] have been used for surface reconstruction [24], [25], [26], object shape estimation [27], and pipeline thickness mapping [28]. The key idea is to represent the surface using a function that specifies whether a point in space is on the surface, outside the surface, or inside the surface.…”

Section: Related Workmentioning

confidence: 99%

Online Informative Path Planning for Active Information Gathering of a 3D Surface

Zhu

Chung

Lawrance

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents an online informative path planning approach for active information gathering on threedimensional surfaces using aerial robots. Most existing works on surface inspection focus on planning a path offline that can provide full coverage of the surface, which inherently assumes the surface information is uniformly distributed hence ignoring potential spatial correlations of the information field. In this paper, we utilize manifold Gaussian processes (mGPs) with geodesic kernel functions for mapping surface information fields and plan informative paths online in a receding horizon manner. Our approach actively plans information-gathering paths based on recent observations that respect dynamic constraints of the vehicle and a total flight time budget. We provide planning results for simulated temperature modeling for simple and complex 3D surface geometries (a cylinder and an aircraft model). We demonstrate that our informative planning method outperforms traditional approaches such as 3D coverage planning and random exploration, both in reconstruction error and information-theoretic metrics. We also show that by taking spatial correlations of the information field into planning using mGPs, the information gathering efficiency is significantly improved.

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“…solve this by partitioning the data into manageable clusters, and training separate local GPs [33,34]. Lee et al [35] propose efficient, incremental updates to GPIS maps via spatial partitioning.…”

Section: Local Gaussian Fieldsmentioning

confidence: 99%

“…Local GP regression: We adopt a spatial partitioning approach similar to [33,34,35]. The scene is divided into L independent local GPs F 1 .…”

Section: B Efficient Online Gpismentioning

confidence: 99%

Tactile SLAM: Real-time inference of shape and pose from planar pushing

Suresh¹,

Bauzá²,

Yu³

et al. 2020

Preprint

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Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixedlag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.

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Online Continuous Mapping using Gaussian Process Implicit Surfaces

Cited by 45 publications

References 22 publications

HMM-Based Dynamic Mapping with Gaussian Random Fields

HMM-Based Dynamic Mapping with Gaussian Random Fields

Online Informative Path Planning for Active Information Gathering of a 3D Surface

Tactile SLAM: Real-time inference of shape and pose from planar pushing

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