Environment mapping using a 3D laser scanner for unmanned ground vehicles

Rejas, José-Ignacio; Sanchez, Alberto; Glez-de-Rivera, Guillermo; Prieto, Manuel; Garrido, Javier

doi:10.1016/j.micpro.2015.10.003

Cited by 11 publications

(4 citation statements)

References 19 publications

(18 reference statements)

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“…Ultrasonic-based localisation is widely used for the application of indoor robot localisation due to the low-cost ultrasonic sensor. However, short detection distance and sensitivity to environmental temperature, humidity and dust all limit the wide application of ultrasonic sensor in AV positioning [55], [56]. Moussa et al [57] used the EKF algorithm to achieve an ultrasonic-based assisted navigation solution.…”

Section: Ultrasonic-based Localisationmentioning

confidence: 99%

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Lü

Smart

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about times, whilst accuracy varies by about 100 times. Vision-and data fusion-based localisation techniques have about 2-5 times potential for improving accuracy compared with lidar-based localisation. Lidar-and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar-and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-toeverything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs' localisation based on the quantitative comparison results are discussed.

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Section: Ultrasonic-based Localisationmentioning

confidence: 99%

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Lü

Smart

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

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“…Pesquisas com imagens de satélite (Forest canopy cover -CC), métrica amplamente usada para várias aplicações de gerenciamento de recursos naturais [26] ou com a tecnologia LiDAR (Light Detection And Ranging) são mais comuns, mas apresentam baixa resolução, no primeiro caso e custos proibitivos no segundo [27,28], apesar de gerar nuvens de pontos 3D e modelos digitais do terreno por meio de feixe laser [29].…”

Section: São Vários Os Normativos Legais Brasileirosunclassified

Fotogrametria com VANT aplicada em estudos ambientais rodoviários / Photogrammetry with UAV applied to environmental road studies

Ascenção¹,

Siqueira²,

Araújo³

et al. 2022

BJDV

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Com a finalidade de se investigar a aplicação da fotogrametria com VANT no âmbito de estudos técnicos e ambientais em rodovias, relacionadas às zonas livres, foram realizadas coletas de imagens com um quadricóptero e sensor embarcados em um veículo terrestre. Os objetivos específicos foram avaliar os resultados de (i) estimativa do diâmetro à altura do peito (DAP) e, respectivamente, a cada árvore selecionada, a (ii) estimativa de sua distância ao bordo da pista, estimativas essas oriundas de uma nuvem de pontos tridimensional gerada a partir das imagens oblíquas terrestres obtidas a curta distância. Para se efetivar as comparações das estimativas supracitadas, foram coletados os valores referentes à verdade de campo usando-se métodos e equipamentos tradicionais. Os resultados das comparações entre os dados estimados através da reconstrução 3D e daqueles pelos métodos convencionais, no âmbito do DAP e da distância ao bordo da pista, se mostraram promissores, com correlação estatística fortíssima e RMSE de 2,62 cm e 8 cm, respectivamente. Conquanto seja importante outros estudos com maior número de indivíduos envolvidos, os resultados apontam para uma possibilidade de aplicação, mesmo sendo um nicho pouco explorado por pesquisadores.

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“…The primary requirement for safety and autonomous flight of the plant protection UAV is the ability to obtain environmental information, actively perceive and understand the environment. Some common sensors, such as millimeter wave (MMV) radar [12], LIDAR [13], ultrasonic [14], and infrared rangefinders [15] have been widely used to detect obstacles. With the development of image processing and airborne computer technology, monocular camera [16], and binocular camera [17] have been used on UAV to help acquire environmental information.…”

Section: Introductionmentioning

confidence: 99%

The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion

Huang

Dong

et al. 2021

Remote Sensing

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To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.

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Environment mapping using a 3D laser scanner for unmanned ground vehicles

Cited by 11 publications

References 19 publications

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Fotogrametria com VANT aplicada em estudos ambientais rodoviários / Photogrammetry with UAV applied to environmental road studies

The Improved A* Obstacle Avoidance Algorithm for the Plant Protection UAV with Millimeter Wave Radar and Monocular Camera Data Fusion

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