カーネル軌道マップによるマルチモーダル確率論的運動予測
Kernel Trajectory Maps for Multi-Modal Probabilistic Motion Prediction
観測された経由点列から将来の軌道のマルチモーダルな確率分布を予測するため、カーネル軌道マップ(KTM)を提案した論文。出力は連続的な確率的過程の混合であり、任意の細かい時間刻みで軌道を問い合わせられる。
著者: Weiming Zhi, Lionel Ott, Fabio Ramos
分類: cs.RO, cs.LG
原文アブストラクト
Understanding the dynamics of an environment, such as the movement of humans and vehicles, is crucial for agents to achieve long-term autonomy in urban environments. This requires the development of methods to capture the multi-modal and probabilistic nature of motion patterns. We present Kernel Trajectory Maps (KTM) to capture the trajectories of movement in an environment. KTMs leverage the expressiveness of kernels from non-parametric modelling by projecting input trajectories onto a set of representative trajectories, to condition on a sequence of observed waypoint coordinates, and predict a multi-modal distribution over possible future trajectories. The output is a mixture of continuous stochastic processes, where each realisation is a continuous functional trajectory, which can be queried at arbitrarily fine time steps.
