A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method

• URL: http://arxiv.org/abs/2506.13100v1
• Date: Mon, 16 Jun 2025 05:11:57 GMT
• Title: A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
• Authors: Zhanhua Xin, Zhihao Wang, Shenghao Zhang, Wanchao Chi, Yan Meng, Shihan Kong, Yan Xiong, Chong Zhang, Yuzhen Liu, Junzhi Yu,
• Abstract summary: This paper proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a ViDAR device.<n>A platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed.
• Score: 21.01092367381287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explored the integration of motor-encoder devices to enhance SLAM performance. By incorporating such devices, it is possible to significantly improve active capability and field of view (FOV) with minimal additional cost and structural complexity. This paper proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a ViDAR (Video Detection and Ranging) device. A ViDAR calibration method is introduced to ensure accurate initialization for VIEO. In addition, a platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed. Experimental data demonstrate that the proposed ViDAR and the VIEO algorithm significantly increase cross-frame co-visibility relationships compared to its corresponding visual-inertial odometry (VIO) algorithm, improving state estimation accuracy. Additionally, the DRL-based active SLAM algorithm, with the ability to decouple from platform motion, can increase the diversity weight of the feature points and further enhance the VIEO algorithm's performance. The proposed methodology sheds fresh insights into both the updated platform design and decoupled approach of active SLAM systems in complex environments.

Related papers

• A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images [4.473596922028091]
  This paper presents a lightweight image fusion algorithm specifically designed for merging visible light and infrared images. The proposed method enhances the generator in a Generative Adversarial Network (GAN) by integrating the Convolutional Block Attention Module. Experiments using the M3FD dataset demonstrate that the proposed algorithm outperforms similar image fusion methods in terms of fusion quality.
  arXiv  Detail & Related papers  (2024-09-07T18:04:39Z)
• Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object Detection [7.135000735428783]
  This paper builds a brain-eye-computer based object detection system for aerial images under few-shot conditions. An adaptive modality balanced online knowledge distillation (AMBOKD) method is proposed to recognize dim objects with the EEG-image data. Experiments conducted on public datasets and system validations in real-world scenarios demonstrate the effectiveness and superiority of our method.
  arXiv  Detail & Related papers  (2024-07-02T02:30:23Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments [55.864869961717424]
  It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. We design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these problems.
  arXiv  Detail & Related papers  (2023-10-23T16:30:39Z)
• Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents [43.137917788594926]
  We propose a tightly-coupled LiDAR-visual SLAM based on geometric features. The entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem. Our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods.
  arXiv  Detail & Related papers  (2023-07-15T10:06:43Z)
• DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System [1.443696537295348]
  This paper presents a robust approach for a visual parallel tracking and mapping (PTAM) system that excels in challenging environments. Our proposed method combines the strengths of heterogeneous multi-modal visual sensors, in a unified reference frame. Our implementation's research-based Python API is publicly available on GitHub.
  arXiv  Detail & Related papers  (2023-06-02T19:52:13Z)
• Robust Visual Odometry Using Position-Aware Flow and Geometric Bundle Adjustment [16.04240592057438]
  A novel optical flow network (PANet) built on a position-aware mechanism is proposed first. Then, a novel system that jointly estimates depth, optical flow, and ego-motion without a typical network to learning ego-motion is proposed. Experiments show that the proposed system not only outperforms other state-of-the-art methods in terms of depth, flow, and VO estimation.
  arXiv  Detail & Related papers  (2021-11-22T12:05:27Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications [78.63280020581662]
  A novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-03-24T08:16:32Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

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.