証拠理論に基づく不確実性を考慮した密で空間的に一貫性のある逆センサモデル
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework
レーダーの疎な検出から、証拠的畳み込みニューラルネットを用いてセンサノイズとモデル不確実性を統合し、少ない時間ステップで密で空間的に一貫した環境状態推定を実現した。
著者: Daniel Bauer, Lars Kuhnert, Lutz Eckstein
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results that show This makes it possible to obtain a denser environment perception in fewer time steps.
