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Multiple sensors are commonly used for three-dimensional (3D)-mapping or robotic-vision applications, as they provide a larger field of view and sufficient observations to fulfill frame-registration and map-updating tasks. However, the data sequences generated by multiple sensors can be inconsistent and contain significant time drift. In this paper, we describe the trajectory drift-compensated strategy that we designed to eliminate the influence of time drift between sensors, remove the inconsistency between the sequences from various sensors, and thereby generate a coarse-to-fine procedure for robust camera-tracking based on two-dimensional-3D observations from stereo sensors. We present the mathematical analysis of the iterative optimizations for pose tracking in a stereo red, green, blue plus depth (RGB-D) camera. Finally, complex indoor scenario experiments demonstrate the efficiency of the proposed stereo RGB-D simultaneous localization and mapping solution. The results verify that the proposed stereo RGB-D mapping solution effectively improves the accuracies of both camera-tracking and 3D reconstruction. [ABSTRACT FROM AUTHOR]