Publicação
DIOR: a hardware-assisted weather denoising solution for LiDAR Point Clouds
| Resumo: | The interest in developing and deploying fully autonomous vehicles on our public roads has come to a full swing. Driverless capabilities, widely spread in modern vehi cles through advanced driver-assistance systems (ADAS), require highly reliable perception features to navigate the environment, being light detection and ranging (LiDAR) sen sors a key instrument in detecting the distance and speed of nearby obstacles and in providing high-resolution 3D rep resentations of the surroundings in real-time. However, and despite being assumed as a game-changer in the autonomous driving paradigm, LiDAR sensors can be very sensitive to adverse weather conditions, which can severely affect the vehicle’s perception system behavior. Aiming at improving the LiDAR operation in challenging weather conditions, which contributes to achieving higher driving automation levels defined by the Society of Automotive Engineers (SAE), this article proposes a weather denoising method called Dynamic light-Intensity Outlier Removal (DIOR). DIOR combines two approaches of the state-of-the-art, the dynamic radius outlier removal (DROR) and the low-intensity outlier removal (LIOR) algorithms, supported by an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, DIOR can outperform state-of-the-art outlier removal solutions, achieving better accuracy and performance while guaranteeing the real-time requirements. |
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| Autores principais: | Roriz, Ricardo João Rei |
| Outros Autores: | Campos, André; Pinto, Sandro; Gomes, Tiago Manuel Ribeiro |
| Assunto: | ADAS Autonomous vehicles FPGA LiDAR Point cloud filtering Weather denoising Meteorology Point cloud compression Laser radar Sensors Noise reduction Three-dimensional displays Heuristic algorithms Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Indústria, inovação e infraestruturas |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The interest in developing and deploying fully autonomous vehicles on our public roads has come to a full swing. Driverless capabilities, widely spread in modern vehi cles through advanced driver-assistance systems (ADAS), require highly reliable perception features to navigate the environment, being light detection and ranging (LiDAR) sen sors a key instrument in detecting the distance and speed of nearby obstacles and in providing high-resolution 3D rep resentations of the surroundings in real-time. However, and despite being assumed as a game-changer in the autonomous driving paradigm, LiDAR sensors can be very sensitive to adverse weather conditions, which can severely affect the vehicle’s perception system behavior. Aiming at improving the LiDAR operation in challenging weather conditions, which contributes to achieving higher driving automation levels defined by the Society of Automotive Engineers (SAE), this article proposes a weather denoising method called Dynamic light-Intensity Outlier Removal (DIOR). DIOR combines two approaches of the state-of-the-art, the dynamic radius outlier removal (DROR) and the low-intensity outlier removal (LIOR) algorithms, supported by an embedded reconfigurable hardware platform. By resorting to field-programmable gate array (FPGA) technology, DIOR can outperform state-of-the-art outlier removal solutions, achieving better accuracy and performance while guaranteeing the real-time requirements. |
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