Greedy-Based Feature Selection for Efficient LiDAR SLAM

• URL: http://arxiv.org/abs/2103.13090v1
• Date: Wed, 24 Mar 2021 11:03:16 GMT
• Title: Greedy-Based Feature Selection for Efficient LiDAR SLAM
• Authors: Jianhao Jiao and Yilong Zhu and Haoyang Ye and Huaiyang Huang and Peng Yun and Linxin Jiang and Lujia Wang and Ming Liu
• Abstract summary: This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems.
• Score: 12.257338124961622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios. However, they commonly have a high latency due to the expensive data association and nonlinear optimization. This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We formulate the feature selection as a combinatorial optimization problem under a cardinality constraint to preserve the information matrix's spectral attributes. The stochastic-greedy algorithm is applied to approximate the optimal results in real-time. To avoid ill-conditioned estimation, we also propose a general strategy to evaluate the environment's degeneracy and modify the feature number online. The proposed feature selector is integrated into a multi-LiDAR SLAM system. We validate this enhanced system with extensive experiments covering various scenarios on two sensor setups and computation platforms. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems. To benefit the community, we have released the source code: https://ram-lab.com/file/site/m-loam.

Related papers

• LLM-based Optimization of Compound AI Systems: A Survey [64.39860384538338]
  In a compound AI system, components such as an LLM call, a retriever, a code interpreter, or tools are interconnected. Recent advancements enable end-to-end optimization of these parameters using an LLM. This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Bidirectional Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
  We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
  arXiv  Detail & Related papers  (2023-05-01T23:20:27Z)
• VP-SLAM: A Monocular Real-time Visual SLAM with Points, Lines and Vanishing Points [31.55798962786664]
  We present a real-time monocular Visual SLAM system that incorporates real-time methods for line and VP extraction. We also present two strategies that exploit vanishing points to estimate the robot's translation and improve its rotation. The proposed system achieves state-of-the-art results and runs in real time, and its performance remains close to the original ORB-SLAM2 system.
  arXiv  Detail & Related papers  (2022-10-23T15:54:26Z)
• Keeping Less is More: Point Sparsification for Visual SLAM [1.370633147306388]
  This study proposes an efficient graph optimization for sparsifying map points in SLAM systems. Specifically, we formulate a maximum pose-visibility and maximum spatial diversity problem as a minimum-cost maximum-flow graph optimization problem. The proposed method works as an additional step in existing SLAM systems, so it can be used in both conventional or learning based SLAM systems.
  arXiv  Detail & Related papers  (2022-07-01T06:39:38Z)
• RISE-SLAM: A Resource-aware Inverse Schmidt Estimator for SLAM [7.388000129690679]
  We present the RISE-SLAM algorithm for performing visual-inertial simultaneous localization and mapping (SLAM) We derive a new consistent approximate method in the information domain, which has linear memory requirements and adjustable (constant to linear) processing cost. In particular, this method, the resource-aware inverse Schmidt estimator (RISE), allows trading estimation accuracy for computational efficiency.
  arXiv  Detail & Related papers  (2020-11-23T21:10:32Z)
• 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)
• Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration [15.946728828122385]
  This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. We validate our approach's performance with extensive experiments on ten sequences (4.60km total length) for the calibration and SLAM. We demonstrate that the proposed work is a complete, robust, and system for various multi-LiDAR setups.
  arXiv  Detail & Related papers  (2020-10-27T13:51:26Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency [23.443265839365054]
  Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency) This paper aims to fill the performance-efficiency gap with an enhancement applied to feature-based VSLAM.
  arXiv  Detail & Related papers  (2020-01-03T03:50:54Z)

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.