TY - GEN
T1 - Efficient Real-Time Localization in Prior Indoor Maps Using Semantic SLAM
AU - Goswami, R. G.
AU - Amith, P. V.
AU - Hari, J.
AU - Dhaygude, A.
AU - Krishnamurthy, P.
AU - Rizzo, J.
AU - Tzes, A.
AU - Khorrami, F.
N1 - Funding Information:
∗ These authors contributed equally. This work is supported in part by the NYUAD Center for Artificial Intelligence and Robotics (CAIR), funded by Tamkeen under the NYUAD Research Institute Award CG010.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, a method for real-time global localization (registration) of an agent (robot or visually impaired person) in the presence of an a priori indoor map (e.g., an image of a floor plan) is presented. In the proposed algorithm, the SLAM map created by the agent equipped with RGB-D and IMU sensors is used in conjunction with an a priori architectural floor plan to find the global location of the agent. This involves extraction and vectorization of the semantic object locations from the a priori map image, implementation of real-time semantic SLAM using onboard sensors on the agent, and the use of a particle filter based optimization method for global localization. The proposed algorithm is applied in an indoor environment and localization results are presented showing the effectiveness of the approach.
AB - In this paper, a method for real-time global localization (registration) of an agent (robot or visually impaired person) in the presence of an a priori indoor map (e.g., an image of a floor plan) is presented. In the proposed algorithm, the SLAM map created by the agent equipped with RGB-D and IMU sensors is used in conjunction with an a priori architectural floor plan to find the global location of the agent. This involves extraction and vectorization of the semantic object locations from the a priori map image, implementation of real-time semantic SLAM using onboard sensors on the agent, and the use of a particle filter based optimization method for global localization. The proposed algorithm is applied in an indoor environment and localization results are presented showing the effectiveness of the approach.
KW - SLAM
KW - indoor environment
KW - localization and navigation
KW - particle filter
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85161283538&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85161283538&partnerID=8YFLogxK
U2 - 10.1109/ICARA56516.2023.10125919
DO - 10.1109/ICARA56516.2023.10125919
M3 - Conference contribution
AN - SCOPUS:85161283538
T3 - 2023 9th International Conference on Automation, Robotics and Applications, ICARA 2023
SP - 299
EP - 303
BT - 2023 9th International Conference on Automation, Robotics and Applications, ICARA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Automation, Robotics and Applications, ICARA 2023
Y2 - 10 February 2023 through 12 February 2023
ER -