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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