An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023-09-17

Autores

José Machado da Silva (Autor) FEUP; Nuno Reis (Autor) FEUP; Correia, M. V. (Autor) FEUP

Revista

Título: Remote Sensing

Vol. 143

Páginas: 1-4570

ISSN: 2072-4292

Editora: MDPI

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 3 Citações

COMPENDEX

Classificação Científica

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia do conhecimento

Projetos Associados

THEIA - Automated Perception Driving

Outras Informações

ID Authenticus: P-00Y-FPZ

DOI: 10.3390/rs15184570

Abstract (EN): The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm's confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18