Intrinsic localization
Intrinsic localization is a method used in mobile laser scanning to recover the trajectory of the scanner, after, or during the measurement. Specifically, it is a way to recover the spatial coordinates and the rotation of the scanner without the use of any other sensors, i.e, extrinsic information. To function in practice, intrinsic localization relies on two things. First, a priori knowledge of the scanning instruments, and second, on sensor data overlap employing simultaneous localization and mapping (SLAM) methods. The term was coined in.[1]
Examples
[edit]Two-dimensional (2D) SLAM is a basic example of intrinsic localization.[2] In three dimensions (3D) with six degrees of freedom, full reconstructions of the environment are feasible [1][3][4]
Other ways
[edit]Other, i.e. extrinsic, ways of localizing mobile laser scanners include using global navigation satellite system (GNSS),[5] inertial measurement unit (IMU),[6] knowledge of the scanner inclination angle,[7] or odometry.
References
[edit]- ^ a b Lehtola, V. V., Virtanen, J. P., Kukko, A., Kaartinen, H., & Hyyppä, H. (2015). Localization of mobile laser scanner using classical mechanics. ISPRS Journal of Photogrammetry and Remote Sensing, 99, 25-29.
- ^ Kohlbrecher, S., Von Stryk, O., Meyer, J., & Klingauf, U. (2011, November). A flexible and scalable slam system with full 3d motion estimation. In 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (pp. 155-160). IEEE
- ^ Lehtola, V. V., Virtanen, J. P., Rönnholm, P., & Nüchter, A. (2016). Localization Corrections for Mobile Laser Scanner Using Local Support-Based Outlier Filtering. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 81-88.
- ^ Lehtola, V. V., Virtanen, J. P., Vaaja, M. T., Hyyppä, H., & Nüchter, A. (2016) Localization of a mobile laser scanner via dimensional reduction. ISPRS Journal of Photogrammetry and Remote Sensing 121, 48–59.
- ^ Kukko, A., Andrei, C. O., Salminen, V. M., Kaartinen, H., Chen, Y., Rönnholm, P., ... & Kosonen, I. (2007). Road environment mapping system of the Finnish Geodetic Institute—FGI Roamer. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 36, 241-247.
- ^ Bosse, M., Zlot, R., & Flick, P. (2012). Zebedee: Design of a spring-mounted 3-d range sensor with application to mobile mapping. IEEE Transactions on Robotics, 28(5), 1104-1119.
- ^ Zhang, J., & Singh, S. (2014, July). LOAM: Lidar odometry and mapping in real-time. In Robotics: Science and Systems Conference (RSS) (pp. 109-111).