This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the Iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings - IEEE International Conference on Robotics and Automation|
|State||Published - Jul 5 2004|
|Event||Proceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States|
Duration: Apr 26 2004 → May 1 2004