Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure

• URL: http://arxiv.org/abs/2001.10249v1
• Date: Tue, 28 Jan 2020 10:30:20 GMT
• Title: Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure
• Authors: Milad Ramezani, Georgi Tinchev, Egor Iuganov and Maurice Fallon
• Abstract summary: We present a factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector. Our system uses only LiDAR sensing and was developed to run on the quadruped robot's navigation PC.
• Score: 7.861777781616249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector to enable a legged robot to localize and map in industrial environments. These facilities can be badly lit and comprised of indistinct metallic structures, thus our system uses only LiDAR sensing and was developed to run on the quadruped robot's navigation PC. Point clouds are accumulated using an inertial-kinematic state estimator before being aligned using ICP registration. To close loops we use a loop proposal mechanism which matches individual segments between clouds. We trained a descriptor offline to match these segments. The efficiency of our method comes from carefully designing the network architecture to minimize the number of parameters such that this deep learning method can be deployed in real-time using only the CPU of a legged robot, a major contribution of this work. The set of odometry and loop closure factors are updated using pose graph optimization. Finally we present an efficient risk alignment prediction method which verifies the reliability of the registrations. Experimental results at an industrial facility demonstrated the robustness and flexibility of our system, including autonomous following paths derived from the SLAM map.

Related papers

• Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis [0.0]
  A random recurrent neural network, called a reservoir, can be used to learn robot movements conditioned on context inputs. In this work, we propose a novel RC-based Learning from Demonstration (LfD) framework.
  arXiv  Detail & Related papers  (2024-11-17T07:25:54Z)
• MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry [2.0508169116681594]
  LiDAR odometry is the task of estimating the ego-motion of the sensor from sequential laser scans. Most of these systems implicitly rely on assumptions about the operating environment, the sensor used, and motion pattern. This paper presents a LiDAR odometry system that can overcome these limitations and operate well under different operating conditions.
  arXiv  Detail & Related papers  (2024-05-09T15:02:26Z)
• UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input [51.150605800173366]
  UnLoc is a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets.
  arXiv  Detail & Related papers  (2023-07-03T04:10:55Z)
• Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments [80.49514665620008]
  Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
  arXiv  Detail & Related papers  (2023-06-20T21:21:19Z)
• IR-MCL: Implicit Representation-Based Online Global Localization [31.77645160411745]
  In this paper, we address the problem of estimating the robots pose in an indoor environment using 2D LiDAR data. We propose a neural occupancy field (NOF) to implicitly represent the scene using a neural network. We show that we can accurately and efficiently localize a robot using our approach surpassing the localization performance of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-10-06T17:59:08Z)
• CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure [5.590924316241286]
  We propose a new real-time LiDAR odometry method called CT-ICP, as well as a complete SLAM with loop closure. The registration is based on scan-to-map with a dense point cloud as map structured in sparse voxels to operate in real time. To show the robustness of the method, we tested it on seven datasets.
  arXiv  Detail & Related papers  (2021-09-27T12:08:26Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)
• LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport [8.21384946488751]
  We introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. Our approach outperforms state-of-the-art techniques by a large margin even while dealing with reverse loops.
  arXiv  Detail & Related papers  (2021-03-08T20:19:37Z)
• 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)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)

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.