Using Texture of Linear Objects for Build Enviroments Model and Navigations

The actual tasks of 3D-reconstruction of the industrial-urban environment and navigation models are considered by solving the identification of textured linear objects in the process of movement according to the onboard complex and technical vision system consisting of a mutually adjusted 3D laser sensor and a video camera with a common viewing area. For a complete solution of the navigation task (determination of three linear and three angular coordinates of the control object), it is necessary to select and identify at least three mutually non-parallel flat objects in the process of moving in a sequence of point clouds formed by a 3D laser sensor. In the case of the allocation of less than three flat objects (for example, in environments subjected to destruction), the navigation problem is not fully solved (not all coordinates are determined unambiguously, and some coordinates are related by linear or non-linear dependencies). In these cases, it is proposed to additionally use the texture of the selected flat objects formed by the video camera. In the paper is given the analysis of the features of the solution of the navigation problem is carried out depending on the number of allocated and identifiable textured linear objects in the current integrated images and algorithms for solving the navigation problem are evaluated for selecting and identifying the process of movement of one textured linear object and of two textured non-parallel linear objects. It is shown that in the first case, the use of texture makes it possible to reduce the solution of the navigational problem to a three-dimensional one, and in the second case to a one-dimensional optimization problem (finding the global optimum of a functional three and one variable, respectively). The proposed algorithms for processing complexed images provide a complete solution to the navigation task even if less than three linear objects are selected, which significantly increases the reliability of solving the navigation task and building an environmental model even in industrial-urban environments that have been destroyed, and therefore, the reliability and survivability of the ground ones and airborne robotic tools in autonomous modes of movement. The results of the corresponding software and hardware solutions in real industrial-urban environments, confirmed the accuracy and effectiveness of the proposed algorithms.

1. Lapshov V. S., Noskov V. P. et al. Boy v gorode. Boevye i obespechivayuschie roboty v usloviyah urbanizirovannoy territorii (Fight in the city. Combat and support robots in urbanized conditions), Izvestiya YuFU Tekhnicheskie nauki, 2011, no. 3, pp. 142—146 (in Russian).

2. Smith R., Self M., Cheeseman P. Estimating uncertain spatial relationships in robotics, Autonomous robot vehicles, 1990, pp. 167—193.

3. Leonard J. J., Durrant-Whyte H. F. Simultaneous map building and localization for an autonomous mobile robot, Proceedings IROS’91: IEEE/RSJ International Workshop on Intelligent Robots and Systems’ 91, 1991, pp. 1442—1447.

4. Kalyaev A. V., Noskov V. P., Chernuhin Yu. V., Kalyaev I. A.Odnorodnye upravlyayuschie struktury adaptivnyh robotov (Homogeneous control structures of adaptive robots), Moscow, Nauka, 1990, p. 147 (in Russian).

5. Lakota N. A., Noskov V. P., Rubtsov I. V., Lundgren Ya.-O., Moor F. Opyt ispol’zovaniya ehlementov iskusstvennogo intellekta v sisteme upravleniya cekhovogo transportnogo robota (Experience of using elements of artificial intelligence in the control system of a workshop transport robot), Mekhatronika, 2000, no. 4, pp. 44—47 (in Russian).

6. Noskov V. P., Rubtsov I. V. Opyt resheniya zadachi avtonomnogo upravleniya dvizheniem mobil’nyh robotov (Experience in solving the problem of autonomous motion control of mobile robots), Mekhatronika, avtomatizaciya, upravlenie, 2005, no. 12, pp. 21—24 (in Russian)

7. Noskov V. P., Noskov A. V. Navigaciya mobil’nyh robotov po dal’nometricheskim izobrazheniyam (Navigation of mobile robots by ranging images), Mekhatronika, avtomatizaciya, upravlenie, 2005, no. 12, pp. 16—21 (in Russian).

8. Zagoruyko S. N., Kaz’min V. N., Noskov V. P. Navigaciya BPLA i 3D-rekonstrukciya vneshney sredy po dannym bortovoy STZ (UAV navigation and 3D-reconstruction of the external environment according to the onboard STV), Mekhatronika, avtomatizaciya, upravlenie, 2014, no. 8, pp. 62—68 (in Russian).

9. Haehnel A. S. D., Thrun S. Generalized ICP, Proceedings of Robotics: Science and Systems (RSS), 2009.

10. Mitra N. J. et al. Registration of point cloud data from a geometric optimization perspective, Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing, ACM, 2004, pp. 22—31.

11. Kaz’min V. N., Noskov V. P. Vydelenie geometricheskih i semanticheskih ob”ektov v dal’nometricheskih izobrazheniyah dlya navigacii robotov i rekonstrukcii vneshney sredy (Selection of geometric and semantic objects in ranging images for robot navigation and reconstruction of the external environment), Izvestiya YuFU. Tekhnicheskie nauki, no. 10 (171), pp. 71—83 (in Russian).

12. Noskov V. P., Kiselev I. O. Trekhmernyy variant metoda Hafa v rekonstrukcii vneshney sredy i navigacii (Three-Dimensional Version of the Hough Method in the Reconstruction of the External Environment and Navigation), Mekhatronika, avtomatizaciya, upravlenie, 2018, no. 8, pp. 552—560 (in Russian).

13. Noskov V. P., Kiselev I. O. Vydelenie ploskih ob”ektov v lineyno strukturirovannyh 3D-izobrazheniyah (Selecting flat objects in linearly structured 3D images), Robototekhnika i tekhnicheskaya kibernetika, no. 2(19), 2018, pp. 31—38 (in Russian).

14. Noskov V. P., Rubtsov I. V., Romanov A.Yu. Formirovanie ob”edinennoy modeli vneshney sredy na osnove informacii videokamery i dal’nomera (Formation of a unified model of the environment based on the information of the video camera and range finder), Mekhatronika, avtomatizaciya, upravlenie, 2007, no. 8, pp. 2—5 (in Russian).

15. Bylow E. et al. Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions, Robotics: Science and Systems, 2013.