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An Extended HOOFR SLAM Algorithm Using IR-D Sensor Data for Outdoor Autonomous Vehicle Localization

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Abstract

Several works have been carried out in the realm of RGB-D SLAM development, yet they have neither been thoroughly assessed nor adapted for outdoor vehicular contexts. This paper proposes an extension of HOOFR SLAM to an enhanced IR-D modality applied to an autonomous vehicle in an outdoor environment. We address the most prevalent camera issues in outdoor contexts: environments with an image-dominant overcast sky and the presence of dynamic objects. We used a depth-based filtering method to identify outlier points based on their depth value. The method is robust against outliers and also computationally inexpensive. For faster processing, we suggest optimization of the pose estimation block by replacing the RANSAC method used for essential matrix estimation with PROSAC. We assessed the algorithm using a self-collected IR-D dataset gathered by the SATIE laboratory instrumented vehicle using a PC and an embedded architecture. We compared the measurement results to those of the most advanced algorithms by assessing translational error and average processing time. The results revealed a significant reduction in localization errors and a significant gain in processing speed compared to the state-of-the-art stereo (HOOFR SLAM) and RGB-D algorithms (Orb-slam2, Rtab-map).

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References

  1. Sobczak, Ł., Filus, K., Domański, A., Domańska, J.: Lidar point cloud generation for slam algorithm evaluation. Sensors (2021)

  2. Ragot, N., Khemmar, R., Pokala, A., Rossi, R., Ertaud, J.-Y.: Benchmark of visual slam algorithms: Orb-slam2 vs rtab-map*. Eighth Int Conf Emerg Secur Technol (2019)

  3. Lee, J., Back, M., Hwang, S.S., Chun, I.Y.: Improved real-time monocular slam using semantic segmentation on selective frames. arXiv (2021)

  4. Younes, G., Asmar, D., Shammas, E., Zelek, J.: Keyframe-based monocular slam: design, survey, and future directions. Robot Auton Syst 98, 67–88 (2017). https://doi.org/10.1016/j.robot.2017.09.010

  5. Mur-Artal, R., Tardos, J.D.: Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Robot (2017)

  6. Yuan, J., Zhu, S., Tang, K., Sun, Q.: Orb-tedm: An rgb-d slam approach fusing orb triangulation estimates and depth measurements. IEEE Trans Instrum Meas (2022)

  7. Nguyen, D.-D., Elouardi, A., Rodriguez, S., Bouaziz, S.: Hoofr slam system: An embedded vision slam algorithm and its hardware-software mapping-based intelligent vehicles applications. IEEE Trans Intell Transp Syst (2019)

  8. Krombach, N., Droeschel, D., Houben, S., Behnke, S.: Feature-based visual odometry prior for real-time semi-dense stereo slam. Robot Auton Syst (2018)

  9. Kaneko, M., Iwami, K., Ogawa, T., Yamasaki, T., Aizawa, K.: Mask-slam: Robust feature-based monocular slam by masking using semantic segmentation. IEEE Comput Soc Conf Comput Vis Pattern Recognit Workshops (2018)

  10. Xiao, L., Wang, J., Qiu, X., Rong, Z., Zou, X.: Dynamic-slam: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. Robot Auton Syst (2019)

  11. Yu, C., Liu, Z., Liu, X.J., Xie, F., Yang, Y., Wei, Q., Fei, Q.: Ds-slam: A semantic visual slam towards dynamic environments. IEEE Int Conf Intell Robot Syst (2018)

  12. Brasch, N., Bozic, A., Lallemand, J., Tombari, F.: Semantic monocular slam for highly dynamic environments. IEEE Int Conf Intell Robot Syst (2018)

  13. Abouzahir, M., Elouardi, A., Latif, R., Bouaziz, S., Tajer, A.: Embedding slam algorithms: Has it come of age?. Robot Auton Syst (2018)

  14. El Bouazzaoui, I., Rodriguez, S., El Ouardi, A.: Enhancing rgb-d slam performances considering sensor specifications for indoor localization. IEEE Sens J (2021)

  15. Labbe, M., Michaud, F.: Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J Field Robot (2019)

  16. Angladon, V., Gasparini, S., Charvillat, V., Pribanic, T., Petkovic, T., Donlic, M., Ahsan, B., Bruel, F.: An evaluation of real-time rgb-d visual odometry algorithms on mobile devices. J Real-Time Image Process (2019)

  17. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: A versatile and accurate monocular slam system. IEEE Trans Robot (2015). https://doi.org/10.1109/TRO.2015.2463671

    Article  Google Scholar 

  18. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of rgb-d slam systems. IEEE/RSJ Int Conf Intell Robot Syst (2012)

  19. Zhang, C.: Pl-gm:rgb-d slam with a novel 2d and 3d geometric constraint model of point and line features. IEEE Access (2021)

  20. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. Proceedings of the IEEE Int Conf Comput Vis (2011)

  21. von Gioi, R.G., Jakubowicz, J., Morel, J.-M., Randall, G.: Lsd: a line segment detector. Image Process Line (2012)

  22. Handa, A., Whelan, T., McDonald, J., Davison, A.J.: A benchmark for rgb-d visual odometry, 3d reconstruction and slam. IEEE Int Conf Robot Autom (2014)

  23. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE trans pattern anal mach intell (2018)

  24. Wei, H., Zhang, T., Zhang, L.: Gmsk-slam: a new rgb-d slam method with dynamic areas detection towards dynamic environments. Multimed Tools Appl (2021)

  25. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell (2017)

  26. Xie, W., Liu, P.X., Zheng, M.: Moving object segmentation and detection for robust rgbd-slam in dynamic environments. IEEE Trans Instrum Meas (2021)

  27. Liu, Y., Wu, Y., Pan, W.: Dynamic rgb-d slam based on static probability and observation number. IEEE Trans Instrum Meass (2021)

  28. Barsan, I.A., Liu, P., Pollefeys, M., Geiger, A.: Robust dense mapping for large-scale dynamic environments. IEEE Int Conf Robot Autom (2019)

  29. Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. IEEE Conf Comput Vis Pattern Recogn (2016)

  30. Nguyen, D.-D., Ouardi, A.E., Aldea, E., Bouaziz, S.: Hoofr: An enhanced bio-inspired feature extractor. 23rd Int Conf Pattern Recogn (2016)

  31. Ulrich, L., Vezzetti, E., Moos, S., Marcolin, F.: Analysis of rgb-d camera technologies for supporting different facial usage scenarios. Multimed Tools Appl (2020)

  32. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. Int J Robot Res (2013)

  33. Grunnet-Jepsen, A., Sweetser, J.N., Woodfill, J.: Best-known-methods for tuning intel® realsense d400 depth cameras for best performance. Intel Corporation, Satan Clara, CA, USA (2018)

  34. Pearson, R.K.: Outliers in process modeling and identification. IEEE Trans control syst technol (2002)

  35. Posio, J., Leiviskä, K., Ruuska, J., Ruha, P.: Outlier detection for 2d temperature data. IFAC proceed vol (2008)

  36. Chum, O., Matas, J.: Matching with prosac - progressive sample consensus. IEEE Comput Soc Conf Comput Vis Pattern Recogn (2005)

  37. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int J Comput Vis (2004)

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Funding

This work was supported by the French Ministry of Higher Education, Research and Innovation.

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Authors and Affiliations

  1. SATIE, CNRS UMR 8029 Paris-Saclay University, Av. des Sciences Batiment 660, Gif-sur-Yvette, Paris, 91190, France

    Imad El Bouazzaoui, Mohammed Chghaf, Sergio Rodriguez & Abdelhafid El Ouardi

  2. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

    Dai Duong Nguyen

Authors
  1. Imad El Bouazzaoui
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  2. Mohammed Chghaf
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  3. Sergio Rodriguez
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  4. Dai Duong Nguyen
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  5. Abdelhafid El Ouardi
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Contributions

All authors contributed to the study conception and methodology. Abdelhafid El Ouardi and Sergio Rodriguez had the idea for the article and supervised the process. Sergio Rodriguez was in charge of providing useful insights to the algorithmic aspects of the discussed SLAM method. Abdelhafid El Ouardi was in charge of providing useful insights to the hardware aspects of the discussed SLAM system. Imad El Bouazzaoui contributed to SLAM algorithms by developing a keypoint filtering method, extending HOOFR SLAM with IR-D sensor data, and creating an outdoor vehicle dataset. He also participated in manuscript writing and editing to share the research findings. Mohammed Chghaf was in charge of synthetizing recent SLAM strategies and the post-processing of the acquired datasets. Dai Duong Nguyen developed the first version of HOOFR-SLAM and reviewed the compatibility of the extended version with a hardware architecture for an embedded application. Sergio Rodriguez and Abdelhafid El Ouardi commented on previous versions of the manuscript and critically revised the work. All authors read and approved the final manuscript.

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Correspondence to Imad El Bouazzaoui.

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El Bouazzaoui, I., Chghaf, M., Rodriguez, S. et al. An Extended HOOFR SLAM Algorithm Using IR-D Sensor Data for Outdoor Autonomous Vehicle Localization. J Intell Robot Syst 109, 56 (2023). https://doi.org/10.1007/s10846-023-01975-3

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