Tightly Coupled 3D Lidar Inertial Odometry and Mapping
arXiv:1904.06993 · doi:10.1109/ICRA.2019.8793511
Abstract
Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.
arXiv commentAccepted by ICRA 2019
References in corpus (2)
Cited by in corpus (57)
- Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping
- Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry
- LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time
- LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
- Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration with Rotating Earth
- Lightweight 3-D Localization and Mapping for Solid-State LiDAR
- The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
- MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping
- Dronument: System for Reliable Deployment of Micro Aerial Vehicles in Dark Areas of Large Historical Monuments
- A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
- Key Ingredients of Self-Driving Cars
- LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
- VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM
- FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
- TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
- GM-Livox: An Integrated Framework for Large-Scale Map Construction with Multiple Non-repetitive Scanning LiDARs
- LIWO: Lidar-Inertial-Wheel Odometry
- NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
- PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation
- LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation
- Intensity Image-based LiDAR Fiducial Marker System
- Improving Self-Consistency in Underwater Mapping Through Laser-Based Loop Closure (Extended)
- MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square
- Ground-SLAM: Ground Constrained LiDAR SLAM for Structured Multi-Floor Environments
- FMCW-LIO: A Doppler LiDAR-Inertial Odometry
- Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
- Metric Monocular Localization Using Signed Distance Fields
- FAST-LIO2: Fast Direct LiDAR-inertial Odometry
- Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping
- Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration
- FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization
- R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
- MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
- VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach
- Learning Parallax for Stereo Event-based Motion Deblurring
- Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation
- SmartDepthSync: Open Source Synchronized Video Recording System of Smartphone RGB and Depth Camera Range Image Frames with Sub-millisecond Precision
- LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry
- Design of an Adaptive Lightweight LiDAR to Decouple Robot-Camera Geometry
- BALM: Bundle Adjustment for Lidar Mapping
- OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
- Deep Neural Networks for Accurate Depth Estimation with Latent Space Features
- AscDAMs: Advanced SLAM-based channel detection and mapping system
- Active Perception with A Monocular Camera for Multiscopic Vision
- A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units
- Point wise or Feature wise? Benchmark Comparison of Public Available LiDAR Odometry Algorithms in Urban Canyons
- Greedy-Based Feature Selection for Efficient LiDAR SLAM
- Eigen-Factors a Bilevel Optimization for Plane SLAM of 3D Point Clouds
- Laser-to-Vehicle Extrinsic Calibration in Low-Observability Scenarios for Subsea Mapping
- Technology Report : Robotic Localization and Navigation System for Visible Light Positioning and SLAM
- MSC-LIO: An MSCKF-Based LiDAR-Inertial Odometry with Same-Plane Cluster Tracking
- AR Mapping: Accurate and Efficient Mapping for Augmented Reality
- Piecewise Linear De-skewing for LiDAR Inertial Odometry
- 3D Surfel Map-Aided Visual Relocalization with Learned Descriptors
- 2Fast-2Lamaa: Large-Scale Lidar-Inertial Localization and Mapping with Continuous Distance Fields
- Monocular Direct Sparse Localization in a Prior 3D Surfel Map
- Cost-effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-range 2D LiDAR