Safe and Reliable Movement of Fast LiDAR-based Self-driving Vehicle

eng Article in English DOI: 10.14313/PAR_253/109

Tomasz Buratowski *, send Mariusz Giergiel *, Piotr Wójcicki *, Jerzy Garus **, Rafał Kot ** * AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Krakow, Poland ** Polish Naval Academy, Śmidowicza Street 69, 81-127 Gdynia, Poland

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Abstract

Classification of objects is an important technique for autonomous ground vehicles to identify a surrounding environment and execute safe path planning. In this paper, a method based on horizontal segmentation is proposed to detect cone-shaped objects in vehicle’s vicinity using a LiDAR sensor. A captured point cloud is divided into five layers based on height information, and the division of detected objects into two groups, cones and others, has been made using classifiers available in MATLAB toolboxes. To separate the classified conical objects into four types used to mark the route, an algorithm for their recognition was developed and used. The proposed solution, verified by navigation experiments in real conditions using an unmanned racing car, has gave good results, i.e., a high rate of cone-shaped objects classification, a short processing time and a low computational load. The performed tests have allowed also to diagnose the causes of incorrect classification of objects. Thus, the experimental results indicated that the approach presented in this work can be used in real time for autonomous, collision-free driving along marked routes.

Keywords

autonomous driving, LIDAR, object classification, wheeled ground vehicle

Bezpieczna i niezawodna jazda szybkiego pojazdu autonomicznego z użyciem systemu LiDAR

Streszczenie

Klasyfikacja obiektów jest ważną technologią dla lądowych pojazdów autonomicznych pozwalającą na identyfikację otaczającego środowiska i zaplanowanie bezpiecznej trasy przejazdu. W artykule zaproponowano metodę klasyfikacji opartą na segmentacji poziomej do wykrywania obiektów w kształcie stożka drogowego w pobliżu pojazdu za pomocą sensora LiDAR. Przechwycona chmura punktów jest dzielona na pięć warstw na podstawie informacji o wysokości, a podziału wykrytych obiektów na dwie grupy, stożki i inne, dokonano z wykorzystaniem klasyfikatorów dostępnych w przybornikach środowiska obliczeniowego MATLAB. Do rozdzielenia sklasyfikowanych obiektów stożkowych na cztery typy, wykorzystywane do oznakowania trasy przejazdu, opracowano i zastosowano algorytm ich rozpoznawania. Zaproponowane metoda, zweryfikowana eksperymentami nawigacyjnymi w warunkach rzeczywistych z wykorzystaniem bezzałogowego samochodu wyścigowego, dała zadowalające wyniki, tj. wysoki poziom klasyfikacji obiektów w kształcie stożka, krótki czas przetwarzania i niską złożoność obliczeniową. Przeprowadzone testy pozwoliły także na zdiagnozowanie przyczyn nieprawidłowej klasyfikacji obiektów stożkopodobnych. Wyniki eksperymentów wykazały, że przedstawione w artykule rozwiązanie może być wykorzystane w czasie rzeczywistym do autonomicznej, bezkolizyjnej jazdy po oznaczonych trasach.

Słowa kluczowe

jazda autonomiczna, klasyfikacja obiektów, kołowy pojazd lądowy, LIDAR

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