MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping

Nguyen, Thien‐Minh; Yuan, Shenghai; Cao, Muqing; Lyu, Yang; Xie, Lihua

doi:10.1109/lra.2021.3080633

Cited by 38 publications

(26 citation statements)

References 15 publications

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“…While the MLOAM method [14] addresses this issue, it focuses purely on lidar and no camera and IMU is involved. In [15], we proposed a multi-input lidar-inertia odometry and mapping scheme called MILIOM, which clearly demonstrates the robustness, accuracy, and real-time performance. Our VIRAL SLAM system is based on this lidar-based system.…”

Section: Related Workmentioning

confidence: 97%

“…A. Sensor data processing 1) Lidar: We refer to our previous work [15] for the details on the processing of lidar messages from multiple sensors. A quick recap is given below.…”

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

“…We note that the FMM process is one of the most time consuming tasks. However, it can be done on parallel threads [15], which can reduce the computation time proportionally with the number of CPU cores used in the system.…”

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

“…When a feature is marginalized, its cost factor is similar to (15), only that the state estimate of the inverse depth λ i is calculated directly from the marginalized 3D coordinates Lf i and kept fixed during the optimization process. Algorithm 1: MMM process on a visual feature Ca f i Input: Ca Z i , λi , Tma , M. Output: Lf i .…”

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

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VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

Nguyen,

Yuan,

Cao

et al. 2021

Preprint

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In this paper, we propose a tightly-coupled, multimodal simultaneous localization and mapping (SLAM) framework, integrating an extensive set of sensors: IMU, cameras, multiple lidars, and Ultra-wideband (UWB) range measurements, hence referred to as VIRAL (visual-inertial-ranginglidar) SLAM. To achieve such a comprehensive sensor fusion system, one has to tackle several challenges such as data synchronization, multi-threading programming, bundle adjustment (BA), and conflicting coordinate frames between UWB and the onboard sensors, so as to ensure real-time localization and smooth updates in the state estimates.To this end, we propose a two stage approach. In the first stage, lidar, camera, and IMU data on a local sliding window are processed in a core odometry thread. From this local graph, new key frames are evaluated for admission to a global map. Visual feature-based loop closure is also performed to supplement the global factor graph with loop constraints. When the global factor graph satisfies a condition on spatial diversity, the BA process will be triggered, which updates the coordinate transform between UWB and onboard SLAM systems. The system then seamlessly transitions to the second stage where all sensors are tightly integrated in the odometry thread. The capability of our system is demonstrated via several experiments on high-fidelity graphical-physical simulation and public datasets.

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Section: Related Workmentioning

confidence: 97%

“…A. Sensor data processing 1) Lidar: We refer to our previous work [15] for the details on the processing of lidar messages from multiple sensors. A quick recap is given below.…”

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

Section: Real-time Localization Function Blocksmentioning

confidence: 99%

See 2 more Smart Citations

VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

Nguyen,

Yuan,

Cao

et al. 2021

Preprint

Self Cite

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“…In this system, the two lidars are mounted at 180 degrees to each other to make up for the self-similar areas with low lidar observability. The authors of [32] proposed a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple lidars, while [33] proposed a scheme to combine multiple lidars with complementary FOV for feature-based lidar-inertia odometry and mapping. While the above methods can work well in single-scene applications, their adaptability to different environments was not considered, especially the environments that need to perceive height information.…”

Section: Introductionmentioning

confidence: 99%

High-Precision SLAM Based on the Tight Coupling of Dual Lidar Inertial Odometry for Multi-Scene Applications

Xiao

Liu

et al. 2022

Applied Sciences

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Simultaneous Localization and Mapping (SLAM) is an essential feature in many applications of mobile vehicles. To solve the problem of poor positioning accuracy, single use of mapping scene, and unclear structural characteristics in indoor and outdoor SLAM, a new framework of tight coupling of dual lidar inertial odometry is proposed in this paper. Firstly, through external calibration and an adaptive timestamp synchronization algorithm, the horizontal and vertical lidar data are fused, which compensates for the narrow vertical field of view (FOV) of the lidar and makes the characteristics of vertical direction more complete in the mapping process. Secondly, the dual lidar data is tightly coupled with an Inertial Measurement Unit (IMU) to eliminate the motion distortion of the dual lidar odometry. Then, the value of the lidar odometry after correcting distortion and the pre-integrated value of IMU are used as constraints to establish a non-linear least-squares objective function. Joint optimization is then performed to obtain the best value of the IMU state values, which will be used to predict the state of IMU at the next time step. Finally, experimental results are presented to verify the effectiveness of the proposed method.

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LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

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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.

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MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping

Cited by 38 publications

References 15 publications

VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM

High-Precision SLAM Based on the Tight Coupling of Dual Lidar Inertial Odometry for Multi-Scene Applications

LiDAR odometry survey: recent advancements and remaining challenges

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