Autonomous vehicles are exposed to various weather during operation, which is
likely to trigger the performance limitations of the perception system, leading
to the safety of the intended functionality (SOTIF) problems. To efficiently
generate data for testing the performance of visual perception algorithms under
various weather conditions, a hierarchical-level rain image generative model,
rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on
the generative adversarial network (GAN) and can generate images of light,
medium, and heavy rain. Different rain intensities are introduced as labels in
conditional GAN (CGAN). Meanwhile, the model structure is optimized and the
training strategy is adjusted to alleviate the problem of mode collapse. In
addition, natural rain images of different intensities are collected and
processed for model training and validation. Compared with the two baseline
models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of
RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the
structural similarity (SSIM) is improved by 18% and 8%, respectively. The
ablation experiments are also carried out to validate the effectiveness of the
model tuning.

通过构建一个基于生成对抗网络的多层级雨图生成模型，实现了在各种天气条件下测试视觉感知算法性能所需的数据生成，该模型能够生成不同强度的轻雨、中雨和大雨图像，并经过优化和调整来改善模式崩溃问题，相比于两个基准模型，测试数据集上 RCCycleGAN 的峰值信噪比 (PSNR) 提高了 2.58 dB 和 0.74 dB，结构相似性指数 (SSIM) 分别提高了 18% 和 8%，并进行了消融实验以验证模型调优的有效性。

基于 GAN 的分层等级雨图像生成模型

Hierarchical-level rain image generative model based on GAN

Semantic object mapping in uncertain, perceptually degraded environments
during long-range multi-robot autonomous exploration tasks such as
search-and-rescue is important and challenging. During such missions, high
recall is desirable to avoid missing true target objects and high precision is
also critical to avoid wasting valuable operational time on false positives.
Given recent advancements in visual perception algorithms, the former is
largely solvable autonomously, but the latter is difficult to address without
the supervision of a human operator. However, operational constraints such as
mission time, computational requirements, mesh network bandwidth and so on, can
make the operator's task infeasible unless properly managed. We propose the
Early Recall, Late Precision (EaRLaP) semantic object mapping pipeline to solve
this problem. EaRLaP was used by Team CoSTAR in DARPA Subterranean Challenge,
where it successfully detected all the artifacts encountered by the team of
robots. We will discuss these results and performance of the EaRLaP on various
datasets.

本文提出了一种名为 EaRLaP 的语义对象映射技术，在多机器人探索中，在含有不确定性、感知降级的环境下实现高召回率和高精度，取得了成功的应用并在不同数据集上进行了表现。