Abstract
In this paper, conditional localization and mapping (CLAM) is realized with a stereo camera as the only sensor. Compared with visual simultaneous localization and mapping (SLAM), the framework of CLAM is a novel proposed condtional filter rather than extended Kalman filter (EKF). In this algorithm, there is no camera velocity information in the filter state, the measurements and state equation all depend on image data which are the most reliable information so that CLAM outperforms SLAM when the camera turns abruptly or there are some frames lost in which conditions the SLAM may diverge quickly because the predefined model is incorrect in such cases. For CLAM, the model is derived from image data so that CLAM has no such problems. The experimental results show that the proposed CLAM is robust to abrupt turning of the camera and frame-losing, and also give the precise 3D information about the features and the trajectory of the camera.
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