FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments

• URL: http://arxiv.org/abs/2401.09322v1
• Date: Wed, 17 Jan 2024 16:46:38 GMT
• Title: FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments
• Authors: Suchetan Saravanan, Corentin Chauffaut, Caroline Chanel, Damien Vivet
• Abstract summary: Active visual SLAM finds a wide array of applications in-Denied sub-terrain environments and outdoor environments for ground robots. It is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. We propose FIT-SLAM, a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments.
• Score: 1.4474137122906163
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Active visual SLAM finds a wide array of applications in GNSS-Denied sub-terrain environments and outdoor environments for ground robots. To achieve robust localization and mapping accuracy, it is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. Through this work, we propose FIT-SLAM (Fisher Information and Traversability estimation-based Active SLAM), a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments. This approach is devised with the dual objectives of sustaining an efficient exploration rate while optimizing SLAM accuracy. Initially, an estimation of a global traversability map is conducted, which accounts for the environmental constraints pertaining to traversability. Subsequently, we propose a goal candidate selection approach along with a path planning method towards this goal that takes into account the information provided by the landmarks used by the SLAM backend to achieve robust localization and successful path execution . The entire algorithm is tested and evaluated first in a simulated 3D world, followed by a real-world environment and is compared to pre-existing exploration methods. The results obtained during this evaluation demonstrate a significant increase in the exploration rate while effectively minimizing the localization covariance.

Related papers

• AG-SLAM: Active Gaussian Splatting SLAM [26.602364433232445]
  We present AG-SLAM, the first active SLAM system utilizing 3D Gaussian Splatting (3DGS) for online scene reconstruction. Our method leverages Fisher Information to balance the dual objectives of maximizing the information gain for the environment while minimizing the cost of localization errors.
  arXiv  Detail & Related papers  (2024-10-22T20:51:45Z)
• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [64.84996994779443]
  We propose a novel Affordances-Oriented Planner for continuous vision-language navigation (VLN) task. Our AO-Planner integrates various foundation models to achieve affordances-oriented low-level motion planning and high-level decision-making. Experiments on the challenging R2R-CE and RxR-CE datasets show that AO-Planner achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-07-08T12:52:46Z)
• Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation [15.623723522165731]
  We introduce a novel framework centered around the Probable Object Location (POLo) score. We further enhance the framework's practicality by introducing POLoNet, a neural network trained to approximate the computationally intensive POLo score. Our experiments, involving the first phase of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet significantly outperforms a range of baseline methods.
  arXiv  Detail & Related papers  (2023-11-14T08:45:32Z)
• SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems [0.0]
  This paper proposes a novel simultaneous exploration and localization approach. It uses information fusion for maximum exploration while performing communication graph optimization for relative localization. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations.
  arXiv  Detail & Related papers  (2023-06-22T01:27:55Z)
• Reinforcement Learning for Agile Active Target Sensing with a UAV [10.070339628481445]
  This paper develops a deep reinforcement learning approach to plan informative trajectories. It exploits its current belief of the target states and incorporates inaccurate sensor models for high-fidelity classification. A unique characteristic of our approach is that it is robust to varying amounts of deviations from the true target distribution.
  arXiv  Detail & Related papers  (2022-12-16T01:01:17Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Uncertainty-driven Planner for Exploration and Navigation [36.933903274373336]
  We consider the problems of exploration and point-goal navigation in previously unseen environments. We argue that learning occupancy priors over indoor maps provides significant advantages towards addressing these problems. We present a novel planning framework that first learns to generate occupancy maps beyond the field-of-view of the agent.
  arXiv  Detail & Related papers  (2022-02-24T05:25:31Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• SOON: Scenario Oriented Object Navigation with Graph-based Exploration [102.74649829684617]
  The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions that depicts step-by-step. This approach deviates from real-world problems in which human-only describes what the object and its surrounding look like and asks the robot to start navigation from anywhere.
  arXiv  Detail & Related papers  (2021-03-31T15:01:04Z)
• POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments [89.43830036483901]
  We focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent and a RGB-D frame. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1.
  arXiv  Detail & Related papers  (2020-09-17T08:23:50Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)

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.