Energy-Based Detection of Adverse Weather Effects in LiDAR Data

Piroli, Aldi; Dallabetta, Vinzenz; Kopp, Johannes; Walessa, M.; Meißner, Daniel; Dietmayer, Klaus

doi:10.1109/lra.2023.3282382

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…The performance of multiple sets of LiDAR sensors under different artificial rainfall conditions was also evaluated in [68], using the number of imaging points of the target as a criterion. Piroli et al [69] detected the presence of rain and snow using an energy-based anomaly detection framework. Li et al [70] evaluated and modeled LiDAR visibility under different artificial fog conditions, while Delecki et al [71] increased pressure on the recognition model by gradually adding computer-synthesized rain, snow, and fog to analyze the causes of recognition failures.…”

Section: Three-dimensional Environmental Perception In Adverse Weathermentioning

confidence: 99%

Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather

Wen,

Peng,

Lin

et al. 2024

Electronics

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Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. In this paper, a multi-modal contrastive learning (CL) strategy, named DHT-CL, is proposed to improve point cloud 3DSS in complex weather for rail-obstacle detection. DHT-CL is a camera and LiDAR sensor fusion strategy specifically designed for complex weather and obstacle detection tasks, without the need for image input during the inference stage. We first demonstrate how the sensor fusion method is more robust under rainy and snowy conditions, and then we design a Dual-Helix Transformer (DHT) to extract deeper cross-modal information through a neighborhood attention mechanism. Then, an obstacle anomaly-aware cross-modal discrimination loss is constructed for collaborative optimization that adapts to the anomaly identification task. Experimental results on a complex weather railway dataset show that with an mIoU of 87.38%, the proposed DHT-CL strategy achieves better performance compared to other high-performance models from the autonomous driving dataset, SemanticKITTI. The qualitative results show that DHT-CL achieves higher accuracy in clear weather and reduces false alarms in rainy and snowy weather.

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Section: Three-dimensional Environmental Perception In Adverse Weathermentioning

confidence: 99%

Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather

Wen,

Peng,

Lin

et al. 2024

Electronics

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“…Non-learning-based methods, such as Kurup et al [5], filter noise points using statistical information like the number of neighbors. Their performance is usually inferior to learning-based approaches [3], and they tend to require a great deal of manual fine-tuning. Deep learning methods like Heinzler et al [1] use a CNNbased approach to detect fog and rainfall in LiDAR point clouds.…”

Section: Related Work a Semantic Segmentation Of Lidar Point Cloudsmentioning

confidence: 99%

“…Deep learning methods like Heinzler et al [1] use a CNNbased approach to detect fog and rainfall in LiDAR point clouds. Piroli et al [3] instead propose an outlier detection framework for identifying adverse weather points like snow, fog, and rain spray. Seppänen et al [2] use successive scans to include temporal information to segment snow points.…”

Section: Related Work a Semantic Segmentation Of Lidar Point Cloudsmentioning

confidence: 99%

“…CNN-based methods like [1], [2] use standard supervised learning to segment adverse weather. Outlier-based methods like [3] rely on a 1 Institute of Measurement, Control, and Microtechnology, Ulm University, Germany {firstname.lastname}@uni-ulm.de 2 BMW AG, Petuelring 130, 80809 Munich, Germany {vinzenz.dallabetta, marc.walessa}@bmw.de and daniel.da.meissner@bmwgroup.com Our proposed method employs three data sources to learn to segment LiDAR scans in adverse weather. First, we use labeled point clouds recorded in adverse weather.…”

Section: Introductionmentioning

confidence: 99%

“…1. We evaluate our approach on the SemanticSpray [3], WADS [5] and SemanticKITTIfog [6] datasets. The results show that our framework can be used in combination with several different FSS approaches.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust 3D Object Detection in Cold Weather Conditions

Piroli

Dallabetta

Walessa

et al. 2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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LiDAR sensors are used in autonomous driving applications to accurately perceive the environment. However, they are affected by adverse weather conditions such as snow, fog, and rain. These everyday phenomena introduce unwanted noise into the measurements, severely degrading the performance of LiDAR-based perception systems. In this work, we propose a framework for improving the robustness of LiDARbased 3D object detectors against road spray. Our approach uses a state-of-the-art adverse weather detection network to filter out spray from the LiDAR point cloud, which is then used as input for the object detector. In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment. In addition to adverse weather filtering, we explore the use of radar targets to further filter false positive detections. Tests on real-world data show that our approach improves the robustness to road spray of several popular 3D object detectors.

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