Marius Brühlmeier

Andrei Cramariuc, Florian Tschopp, Nikhilesh Alatur et al.

Localization is an essential task for mobile autonomous robotic systems that want to use pre-existing maps or create new ones in the context of SLAM. Today, many robotic platforms are equipped with high-accuracy 3D LiDAR sensors, which allow a geometric mapping, and cameras able to provide semantic cues of the environment. Segment-based mapping and localization have been applied with great success to 3D point-cloud data, while semantic understanding has been shown to improve localization performance in vision based systems. In this paper we combine both modalities in SemSegMap, extending SegMap into a segment based mapping framework able to also leverage color and semantic data from the environment to improve localization accuracy and robustness. In particular, we present new segmentation and descriptor extraction processes. The segmentation process benefits from additional distance information from color and semantic class consistency resulting in more repeatable segments and more overlap after re-visiting a place. For the descriptor, a tight fusion approach in a deep-learned descriptor extraction network is performed leading to a higher descriptiveness for landmark matching. We demonstrate the advantages of this fusion on multiple simulated and real-world datasets and compare its performance to various baselines. We show that we are able to find 50.9% more high-accuracy prior-less global localizations compared to SegMap on challenging datasets using very compact maps while also providing accurate full 6 DoF pose estimates in real-time.

José L. Vázquez, Marius Brühlmeier, Alexander Liniger et al.

In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.