Abstract-Developing the perfect SLAM front-end that produces graphs which are free of outliers is generally impossible due to perceptual aliasing. Therefore, optimization back-ends need to be able to deal with outliers resulting from an imperfect frontend. In this paper, we introduce dynamic covariance scaling, a novel approach for effective optimization of constraint networks under the presence of outliers. The key idea is to use a robust function that generalizes classical gating and dynamically rejects outliers without compromising convergence speed. We implemented and thoroughly evaluated our method on publicly available datasets. Compared to recently published state-of-theart methods, we obtain a substantial speed up without increasing the number of variables in the optimization process. Our method can be easily integrated in almost any SLAM back-end.

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Robots are expected to operate autonomously in increasingly complex scenarios such as crowded streets or heavy traffic situations. Perceiving the dynamics of moving objects in the environment is crucial for safe and smart navigation and therefore a key enabler for autonomous driving. In this paper we present a novel model-free approach for detecting and tracking dynamic objects in 3D LiDAR scans obtained by a moving sensor. Our method only relies on motion cues and does not require any prior information about the objects. We sequentially detect multiple motions in the scene and segment objects using a Bayesian approach. For robustly tracking objects, we utilize their estimated motion models. We present extensive quantitative results based on publicly available datasets and show that our approach outperforms the state of the art.

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Robot localization systems typically assume that the environment is static, ignoring the dynamics inherent in most real-world settings. Corresponding scenarios include households, offices, warehouses and parking lots, where the location of certain objects such as goods, furniture or cars can change over time. These changes typically lead to inconsistent observations with respect to previously learned maps and thus decrease the localization accuracy or even prevent the robot from globally localizing itself. In this paper we present a sound probabilistic approach to lifelong localization in changing environments using a combination of a Rao-Blackwellized particle filter with a hidden Markov model. By exploiting several properties of this model, we obtain a highly efficient map management approach for dynamic environments, which makes it feasible to run our algorithm online. Extensive experiments with a real robot in a dynamically changing environment demonstrate that our algorithm reliably adapts to changes in the environment and also outperforms the popular Monte-Carlo localization approach.