RI-LIO: Reflectivity Image Assisted Tightly-Coupled LiDAR-Inertial Odometry

Zhang, Yanfeng; Tian, Yunong; Wang, Wanguo; Yang, Guodong; Li, Zhishuo; Jing, Fengshui; Tan, Min

doi:10.1109/lra.2023.3243528

Cited by 13 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compare our G-iRIOM method with the EKFRIO method [Doer and Trommer, 2020b], which is the best-known and opensource 4D radar inertial odometry method. Moreover, we compare two variants of our previously proposed 4D radar inertial mapping and positioning method [Zhuang et al, 2023]: the iRIO method without the loop closure module and the iRIOM method with the loop closure module. Furthermore, to test the competitiveness of our radar-based multi-sensor fusion localization method with the existing popular lidar method, we compare it with the FastLIO-SLAM method that fuses loop closure information.…”

Section: Results Evaluationmentioning

confidence: 99%

“…The point cloud matching of 4D imaging radars has a 3D search space instead of 2D, and suffers from various noises, making it hard to achieve stable and accurate scan matching. 4D iRIOM [Zhuang et al, 2023] and 4DRadarSLAM [Zhang et al, 2023] enhanced GICP by considering the probability distribution of the point cloud for more stable scan matching. Some works used inertial data with scan-to-scan matching to make the odometry more robust and reduce trajectory drift for missing data and wrong matches.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with the loosely coupled approach used by the 4D iRIOM [Zhuang et al, 2023] and EKFRIO [Doer and Trommer, 2020b] methods, we establish the 1D residual equations for the radar observations directly from the original Doppler velocity measurements, without using methods such as least squares to calculate the radar ego-velocity observations (3D) for a single frame in advance. This tightly coupled approach makes the Doppler velocity constraint more uniform and dense, effectively enhancing the consistency between the optimized state and the local observations.…”

Section: Doppler Velocity Measurementmentioning

confidence: 99%

“…As in 4D iRIOM [Zhuang et al, 2023], we use a scan-to-submap matching method and a distribution-to-multi-distribution spatial weighting strategy. On this basis, we use RCS to weight the radar point matching residuals defined in iRIOM, thus reducing the impact of matches with large RCS differences on the state updating process, as we consider these matches to be unreliable.…”

Section: Radar Point Matchingmentioning

confidence: 99%

See 3 more Smart Citations

4d Radar/Imu/GNSS Integrated Positioning and Mapping for Large-Scale Environments

Wang,

Zhuang,

El-Bendary

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. 4D millimeter-wave radar can work in harsh weather conditions such as fog and snow, and measure the position and radial Doppler velocity of objects in three-dimensional space. Some existing methods perform positioning by fusing 3D velocity or point cloud matching information estimated by 4D radar with IMU information. However, the sparsity of radar point clouds and the interference of large amount of noise lead to low accuracy of odometry, and most of the existing work cannot achieve the global consistent mapping with radar point clouds. In this paper, a 4D radar/IMU/GNSS localization and mapping method, G-iRIOM, is proposed. We tightly coupled IMU measurement, Doppler observation information and point cloud matching information from 4D radar by iterative extended Kalman filtering (iEKF) method, and introduced GNSS RTK and loop closure information as global observation to correct the positioning drift of the odometry. The experimental results show that the tightly coupled Doppler velocity information can effectively improve the control of the pose on the local point cloud position, thus enhancing the mapping accuracy. Meanwhile, the introduction of GNSS and loop closure information can significantly improve the positioning accuracy of 4D radar odometry as a kind of global observation, and achieve global consistent mapping of large-scale outdoor scenes based on 4D radar point clouds.

show abstract

Section: Results Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Doppler Velocity Measurementmentioning

confidence: 99%

Section: Radar Point Matchingmentioning

confidence: 99%

See 2 more Smart Citations

4d Radar/Imu/GNSS Integrated Positioning and Mapping for Large-Scale Environments

Wang,

Zhuang,

El-Bendary

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Doppler information has also been exploited in the literature to solve odometry. For instance, [24] exploits landmark velocities to assist a radar-based Simulaneous Localisation And Mapping (SLAM) pipeline; yet the estimates are given as a pointcloud, and the raw data is not accessed directly. Doppler has also been explored in a recent work on FMCW LiDAR [25], where the Doppler velocity measurements are used to assist relative pose estimations.…”

Section: Related Workmentioning

confidence: 99%

Doppler-Aware Odometry from FMCW Scanning Radar

Rennie,

Williams,

Newman

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

This work explores Doppler information from a millimetre-Wave (mm-W) Frequency-Modulated Continuous-Wave (FMCW) scanning radar to make odometry estimation more robust and accurate. Firstly, doppler information is added to the scan masking process to enhance correlative scan matching. Secondly, we train a Neural Network (NN) for regressing forward velocity directly from a single radar scan; we fuse this estimate with the correlative scan matching estimate and show improved robustness to bad estimates caused by challenging environment geometries, e.g. narrow tunnels. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets.

show abstract

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

View full text Add to dashboard Cite

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

show abstract

RI-LIO: Reflectivity Image Assisted Tightly-Coupled LiDAR-Inertial Odometry

Cited by 13 publications

References 22 publications

4d Radar/Imu/GNSS Integrated Positioning and Mapping for Large-Scale Environments

4d Radar/Imu/GNSS Integrated Positioning and Mapping for Large-Scale Environments

Doppler-Aware Odometry from FMCW Scanning Radar

LiDAR odometry survey: recent advancements and remaining challenges

Contact Info

Product

Resources

About