Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms

Li, Qingqing; Yu, Xianjia; Queralta, Jorge Peña; Westerlund, Tomi

doi:10.1109/iros47612.2022.9981078

Cited by 13 publications

(17 citation statements)

References 28 publications

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“…This work is, to the best of our knowledge, the first multi-LiDARinertial SLAM system able to effectively integrate LiDAR sensors with heterogeneous scan modalities within a single estimation and optimization framework. This work is inspired by the limitations found in state-of-the-art algorithms for different LiDAR sensors in our previous works [15], [11], where we show that low-cost solid-state LiDARs outperform high-resolution spinning LiDAR in an outdoor environment, while at the same time perform poorly in indoor environments. The unique characteristics and main contributions of our work can be summarized as follows: introduces our proposed mythology.…”

Section: Introductionmentioning

confidence: 85%

“…The design of our system is motivated by our previous work [15] where solid-state LiDAR shows significant performance outdoors but failed all tests indoors. To combine the high situation awareness ability and robust performance, here we proposed multi-modal LiDAR-inertial odometry and mapping scheme.…”

Section: A Overviewmentioning

confidence: 99%

“…From our previous research [15], [11], a tightly coupled solid-state LiDAR-inertial system shows competitive performance outdoors but poorly in indoor environments. Therefore, here we aim to compare our proposed system with a typical and challenging indoor environment: an office room, a long corridor, and a large hall.…”

Section: B Qualitative Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Li¹,

Yu²,

Queralta³

et al. 2023

Preprint

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Section: Introductionmentioning

confidence: 85%

Section: A Overviewmentioning

confidence: 99%

See 1 more Smart Citation

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Li¹,

Yu²,

Queralta³

et al. 2023

Preprint

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“…We tested the reliability of our algorithm with the open source dataset tiers-LIDARs-dataset [40] and on the device shown in figure 6.…”

Section: Methodsmentioning

confidence: 99%

Dual LIDAR online calibration and mapping and perception system

Zheng

et al. 2023

Meas. Sci. Technol.

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Making autonomous mobile devices capable of autonomous positioning and map building in a GPS-denied environment as well as being able to circumvent people in the operating area simultaneously, is essential for many UGV appliances. In this paper, a mapping and positioning system using dual LIDAR is proposed for positioning and building maps. The system may also detect people in the operating space of the equipment using infrared heat maps. The localization and mapping information from each LIDAR is unified in a loosely coupled approach after simultaneous extrinsic calibration. We propose a method for constructing factor graph using LIDAR point cloud geometric features to optimally solve the dual LIDAR extrinsic in real time. We tested the localization accuracy with a publicly available dataset and three real-world scenarios and compared it with three existing methods. The test results in open-source datasets and real scenarios show that our proposed method improves the position and attitude estimation by up to 50% compared to the three existing methods, and the proposed dual LIDAR extrinsic calibration method can achieve an estimation accuracy of 0.05m for the translational extrinsic and 1deg for the rotational extrinsic. In addition, our proposed method achieves well position and attitude estimation when other existing methods show severe drift in trajectory estimation in real scenarios. Thus, our proposed method is suitable for obtaining high accuracy measurements of LIDAR extrinsic parameters and for dense mapping and accurate localization in the environment of GNSS-denied and human mobility.

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“…For instance, the UrbanNav dataset [55] features three mechanical LiDARs navigating urban landscapes, presenting challenges due to asynchronous multiple LiDARs. The Tiers dataset [112] employs a combination of three mechanical and three scanning solid-state LiDARs, capturing distinct measurements from identical locations and offering a unique perspective. On a larger scale, the HeLiPR dataset [65] includes a variety of structured environments and introduces FMCW LiDAR, providing the opportunity to utilize velocity information for LiDAR odometry.…”

Section: Public Datasetsmentioning

confidence: 99%

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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Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms

Cited by 13 publications

References 28 publications

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments

Dual LIDAR online calibration and mapping and perception system

LiDAR odometry survey: recent advancements and remaining challenges

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