Abstract
In this work, we have developed a new observation model for a stereo-based simultaneous localization and mapping (SLAM) system within the standard Extended-Kalman filter (EKF) framework. The observation model was derived by using the inverse depth parameterization as the landmark model, and contributes to both bearing and range information into the EKF estimation. In this way the inherently non-linear problem cause by the camera projection equations is resolved and real depth uncertainty distribution of landmarks features can be accurately estimated. The system was tested by real-world large-scale outdoor data. Analysis results show that the landmark feature depth estimation is more stable and the uncertainty noise converges faster than the binocular stereo-based approach. We also found minor drift in the vehicle pose estimation even after extended periods demonstrating the effectiveness of the new model.
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