UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes

Wen, Weisong; Zhou, Yiyang; Zhang, Guohao; Fahandezh-Saadi, Saman; Bai, Xiwei; Zhan, Wei; Tomizuka, Masayoshi; Hsu, Li‐Ta

doi:10.1109/icra40945.2020.9196526

Cited by 106 publications

(71 citation statements)

References 24 publications

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“…Insufficient positioning accuracy [1] in urban canyon areas is still one of the key problems that postpone the arrival of the large population of autonomous systems [2]. Light detection and ranging (LiDAR), camera, and inertial navigation system (INS) [3] are usually integrated with global navigation satellite system (GNSS) positioning [4][5][6][7] to obtain robust positioning.…”

Section: Introductionmentioning

confidence: 99%

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai

Wen

Hsu

2020

IET Intelligent Transport Systems

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“…As this paper analyzed only one dataset collected in a typical urban canyon of Hong Kong, it will be interesting and necessary to determine how FGO will handle multiple datasets from diverse urban scenarios. In the future, we will examine FGO performance using more sensors (e.g., LiDAR) with our recently published UrbanLoco dataset (Wen et al., 2020), which comprises a complete set of vehicular navigation sensor data collected in both Hong Kong and downtown San Francisco.…”

Section: Discussionmentioning

confidence: 99%

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

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Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an “EKF‐like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

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“…2. The 3D LiDAR (e.g., Livox Horizon) streams out point clouds at a typical frequency of 10 Hz and is synchronized with a six-axis IMU providing gyroscope and accelerometer readings at higher frequency (e.g., 200 Hz for Xsens MTi-670 5 ). We want to estimate the six-DoF egomotion of the LiDAR frame and obtain a globally consistent map simultaneously.…”

Section: System Pipelinementioning

confidence: 99%

“…LIO-SAM requires nine-axis IMU measurements. Thus, we include the UrbanLoco and UrbanNav data sets [5] recorded using a HDL-32E and Xsens MTi-10 IMU (nine-axis, 100 Hz). The RMSE of the absolute position error (APE) is computed for the final estimated trajectory based on the ground truth using the script in [27].…”

Section: B Public Data Setmentioning

confidence: 99%

“…E STIMATING six-DoF egomotion plays a fundamental role in a wealth of applications ranging from robot navigation and inspection to virtual/augmented reality, see [1]- [4]. With the booming of autonomous driving, light detection and ranging (LiDAR) sensors have gained tremendous popularity [5], [6]. Compared to visual sensors, 3D LiDARs provide lightinginvariant and accurate perception of the surroundings with long detection range and high robustness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping

Hanebeck

2021

IEEE Robot. Autom. Lett.

195

100

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We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state and mechanical LiDARs. As frontend, a feature-based lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe selection. As backend, a hierarchical keyframe-based sliding window optimization is performed through marginalization for directly fusing IMU and LiDAR measurements. For the Livox Horizon, a newly released solid-state LiDAR, a novel feature extraction method is proposed to handle its irregular scan pattern during preprocessing. LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both LiDAR types on public data sets of mechanical LiDARs and in experiments using the Livox Horizon. Source code and recorded experimental data sets are available at https://github.com/KIT-ISAS/lili-om.

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UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes

Cited by 106 publications

References 24 publications

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping

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