TY - JOUR
T1 - Parking spot estimation and mapping method for mobile robots
AU - Westfechtel, Thomas
AU - Ohno, Kazunori
AU - Mizuno, Naoki
AU - Hamada, Ryunosuke
AU - Kojima, Shotaro
AU - Tadokoro, Satoshi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.
AB - Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.
KW - AI-based methods
KW - big data in robotics and automation
KW - intelligent transportation systems
KW - mapping
KW - semantic scene. understanding
UR - http://www.scopus.com/inward/record.url?scp=85063308236&partnerID=8YFLogxK
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U2 - 10.1109/LRA.2018.2849832
DO - 10.1109/LRA.2018.2849832
M3 - Article
AN - SCOPUS:85063308236
VL - 3
SP - 3371
EP - 3378
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 4
ER -