LIBRE: The Multiple 3D LiDAR Dataset

Carballo, Alexander; Lambert, Jacob; Monrroy, Abraham; Wong, David; Narksri, Patiphon; Kitsukawa, Yuki; Takeuchi, Eijiro; Kato, Shigeo; Takeda, Kazuya

doi:10.48550/arxiv.2003.06129

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…However, the problem with using artificial weather conditions is that they have to be validated themselves first. As stated in [10], that is not always the case. In their lidar measurements in a weather chamber, "rain pillars" are clearly visible underneath the nozzles of the rain generator and it is also not guaranteed that drop size distributions match with real conditions.…”

Section: Introductionmentioning

confidence: 94%

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Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Linnhoff

Hofrichter

Elster

et al. 2022

Sensors

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Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.

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Section: Introductionmentioning

confidence: 94%

“…However, they did not test automotive lidars with an included signal processing. Tests with actual lidar sensors in weather chambers were performed in [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Outdoor devices to generate synthetic rain where developed in [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Linnhoff

Hofrichter

Elster

et al. 2022

Sensors

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“…The quality of LiDAR point clouds and camera images can also degenerate significantly, for instance, when facing a realistic density of fog particles, raindrops and snowflakes in mist, rain and snow. Studies of the degradation of LiDAR sensors are performed in (Jokela et al (2019); Carballo et al (2020)) and suggest that the data of all the tested LiDAR sensors degrades, to some extent, in foggy, rainy and snowy conditions. Given the fact that a motion prior is mainly effective in addressing short-period and temporary sensor degradation, even visual-inertial or LiDAR-inertial SLAM systems are anticipated to fail in these challenging weather conditions.…”

Section: Introductionmentioning

confidence: 99%

RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions

Hong

Pétillot

Wallace

et al. 2022

The International Journal of Robotics Research

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A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

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“…Lidar is an accurate and fast long-range sensor but struggles to perceive the environment in presence of dust or in harsh weather [11]. Vision is moderately accurate and inexpensive but operates at shorter range and is sensitive to illumination changes or weather conditions.…”

Section: Introductionmentioning

confidence: 99%

CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry

Adolfsson¹,

Magnusson²,

Alhashimi³

et al. 2021

Preprint

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This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for largescale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55 Hz merely on a single laptop CPU thread.

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LIBRE: The Multiple 3D LiDAR Dataset

Cited by 5 publications

References 14 publications

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors

RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions

CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry

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