Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process

• URL: http://arxiv.org/abs/2503.04134v1
• Date: Thu, 06 Mar 2025 06:26:57 GMT
• Title: Real-time Spatial-temporal Traversability Assessment via Feature-based Sparse Gaussian Process
• Authors: Senming Tan, Zhenyu Hou, Zhihao Zhang, Long Xu, Mengke Zhang, Zhaoqi He, Chao Xu, Fei Gao, Yanjun Cao,
• Abstract summary: Terrain analysis is critical for the practical application of ground mobile robots in real-world tasks.<n>We propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to navigate through complex terrains.<n>We develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor environments.
• Score: 14.428139979659395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terrain analysis is critical for the practical application of ground mobile robots in real-world tasks, especially in outdoor unstructured environments. In this paper, we propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to effectively navigate through complex terrains. Our approach utilizes sparse Gaussian processes (SGP) to extract geometric features (curvature, gradient, elevation, etc.) directly from point cloud scans. These features are then used to construct a high-resolution local traversability map. Then, we design a spatial-temporal Bayesian Gaussian kernel (BGK) inference method to dynamically evaluate traversability scores, integrating historical and real-time data while considering factors such as slope, flatness, gradient, and uncertainty metrics. GPU acceleration is applied in the feature extraction step, and the system achieves real-time performance. Extensive simulation experiments across diverse terrain scenarios demonstrate that our method outperforms SOTA approaches in both accuracy and computational efficiency. Additionally, we develop an autonomous navigation framework integrated with the traversability map and validate it with a differential driven vehicle in complex outdoor environments. Our code will be open-source for further research and development by the community, https://github.com/ZJU-FAST-Lab/FSGP_BGK.

Related papers

• A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization [8.221371036055167]
  We propose a semantic-temporal trajectory planning method based on graph optimization.<n>It can effectively handle complex urban public road scenarios and perform in real time.<n>We will release our codes to accommodate benchmarking for the research community.
  arXiv  Detail & Related papers  (2025-02-25T12:27:06Z)
• Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing [19.786955745157453]
  We present a novel algorithm for real-time planar semantic mapping tailored for humanoid robots navigating complex terrains such as staircases. We utilize an anisotropic diffusion filter on depth images to effectively minimize noise from gradient jumps while preserving essential edge details. Our approach achieves real-time performance, processing single frames at rates exceeding $30Hz$, which facilitates detailed plane extraction and map management swiftly and efficiently.
  arXiv  Detail & Related papers  (2024-11-04T09:34:55Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model [44.490978394267195]
  We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
  arXiv  Detail & Related papers  (2023-04-23T08:42:45Z)
• Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU [1.841057463340778]
  This paper proposes and evaluates a real-time machine learning-based Traversability Analysis method. We show that integrating a new set of geometric and visual features and focusing on important implementation details enables a noticeable boost in performance and reliability. The proposed approach has been compared with state-of-the-art Deep Learning approaches on a public dataset of outdoor driving scenarios.
  arXiv  Detail & Related papers  (2022-06-07T07:57:34Z)
• Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy [48.51396198176273]
  We propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability.
  arXiv  Detail & Related papers  (2021-09-14T07:24:53Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments [11.042142015353626]
  We propose a deep learning framework, trained in an end-to-end fashion from elevation maps and trajectories, to estimate the occurrence of failure events. We show that transferring and fine-tuning of an application-independent pre-trained model retains better performance than training uniquely on scarcely available real data.
  arXiv  Detail & Related papers  (2021-05-23T13:49:05Z)
• Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform [62.91011959772665]
  Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles. Current approaches that operate directly on the point cloud use complex spatial aggregation operations. We propose a projection-based method, called Multi-scale Interaction Network (MINet), which is very efficient and accurate.
  arXiv  Detail & Related papers  (2020-08-20T19:06:11Z)
• 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)

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.