In this paper we propose a physically motivated sensor model of Time-of-Flight cameras. We provide methods to calibrate our proposed model and compensate all modeled effects. This enables us to reliably detect and filter out inconsistent measurements and to record high dynamic range (HDR) images. We believe that HDR images have a significant benefit especially for mapping narrow-spaced environments as in urban search and rescue. We provide methods to invert our model in real-time and gain significantly higher precision than using the vendor-provided sensor driver. In contrast to previously published purely phenomenological calibration methods our model is physically motivated and thus better captures the structure of the different effects involved.

This paper shows that the field of visual SLAM has matured enough to build a visual SLAM system from open source components. The system consists of feature detection, data association, and sparse bundle adjustment. For all three modules we evaluate different libraries w.r.t. ground truth.

In this paper, we present a new hierarchical optimization solution to the graph-based simultaneous localization and mapping (SLAM) problem. During online mapping, the approach corrects only the coarse structure of the scene and not the overall map. In this way, only updates for the parts of the map that need to be considered for making data associations are carried out. The hierarchical approach provides accurate non-linear map estimates while being highly efficient. Our error minimization approach exploits the manifold structure of the underlying space. In this way, it avoids singularities in the state space parameterization. The overall approach is accurate, efficient, designed for online operation, overcomes singularities, provides a hierarchical representation, and outperforms a series of state-of-the-art methods.