Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes

• URL: http://arxiv.org/abs/2503.16613v1
• Date: Thu, 20 Mar 2025 18:10:13 GMT
• Title: Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes
• Authors: Ashten Akemoto, Frances Zhu,
• Abstract summary: This study evaluates an informative path planning algorithm for mapping a scalar variable distribution.<n>We compare traditional open loop coverage methods with information-theoretic approaches.<n>The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many environments, such as unvisited planetary surfaces and oceanic regions, remain unexplored due to a lack of prior knowledge. Autonomous vehicles must sample upon arrival, process data, and either transmit findings to a teleoperator or decide where to explore next. Teleoperation is suboptimal, as human intuition lacks mathematical guarantees for optimality. This study evaluates an informative path planning algorithm for mapping a scalar variable distribution while minimizing travel distance and ensuring model convergence. We compare traditional open loop coverage methods (e.g., Boustrophedon, Spiral) with information-theoretic approaches using Gaussian processes, which update models iteratively with confidence metrics. The algorithm's performance is tested on three surfaces, a parabola, Townsend function, and lunar crater hydration map, to assess noise, convexity, and function behavior. Results demonstrate that information-driven methods significantly outperform naive exploration in reducing model error and travel distance while improving convergence potential.

Related papers

• A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning [24.111048817721592]
  Federated learning heavily relies on distributed gradient descent techniques. In the situation where gradient information is not available, gradients need to be estimated from zeroth-order information. We propose a non-isotropic sampling method to improve the gradient estimation procedure.
  arXiv  Detail & Related papers  (2024-09-24T10:36:40Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Efficient Prior Calibration From Indirect Data [5.588334720483076]
  This paper is concerned with learning the prior model from data, in particular, learning the prior from multiple realizations of indirect data obtained through the noisy observation process. An efficient residual-based neural operator approximation of the forward model is proposed and it is shown that this may be learned concurrently with the pushforward map.
  arXiv  Detail & Related papers  (2024-05-28T08:34:41Z)
• Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments [49.61405888107356]
  We release a dataset of simulated path loss radio maps together with realistic city maps from real-world locations and aerial images from open datasources. Initial experiments regarding model architectures, input feature design and estimation of radio maps from aerial images are presented.
  arXiv  Detail & Related papers  (2024-01-12T14:56:45Z)
• Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem [63.99794069984492]
  The Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community.<n>We crash-test existing continuous GWOT approaches on different scenarios, carefully record and analyze the obtained results, and identify issues.<n>We propose a new continuous GWOT method which does not rely on discrete techniques and partially solves some of the problems of the competitors.
  arXiv  Detail & Related papers  (2023-03-10T15:21:12Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Residual Overfit Method of Exploration [78.07532520582313]
  We propose an approximate exploration methodology based on fitting only two point estimates, one tuned and one overfit. The approach drives exploration towards actions where the overfit model exhibits the most overfitting compared to the tuned model. We compare ROME against a set of established contextual bandit methods on three datasets and find it to be one of the best performing.
  arXiv  Detail & Related papers  (2021-10-06T17:05:33Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments [17.276441789710574]
  The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. This paper presents a method to solve the loop closure problem using only spatial information.
  arXiv  Detail & Related papers  (2020-09-01T04:41:40Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)
• Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data [13.56926815833324]
  We learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses. Instead of inserting all arriving data into the GP models, we greedily trade-off between model complexity and prediction error. The results show that we can learn compact and accurate implicit surface models under different conditions.
  arXiv  Detail & Related papers  (2020-02-12T11:06:48Z)
• Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization [18.259478519717426]
  This paper introduces a novel and distributed method for detecting inter-map loop closure outliers in simultaneous localization and mapping (SLAM) The proposed algorithm does not rely on a good initialization and can handle more than two maps at a time.
  arXiv  Detail & Related papers  (2020-02-07T06:34:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.