In autonomous and mobile robotics, a principal challenge is resilient
real-time environmental perception, particularly in situations characterized by
unknown and dynamic elements, as exemplified in the context of autonomous drone
racing. This study introduces a perception technique for detecting drone racing
gates under illumination variations, which is common during high-speed drone
flights. The proposed technique relies upon a lightweight neural network
backbone augmented with capabilities for continual learning. The envisaged
approach amalgamates predictions of the gates' positional coordinates,
distance, and orientation, encapsulating them into a cohesive pose tuple. A
comprehensive number of tests serve to underscore the efficacy of this approach
in confronting diverse and challenging scenarios, specifically those involving
variable lighting conditions. The proposed methodology exhibits notable
robustness in the face of illumination variations, thereby substantiating its
effectiveness.

在自主移动机器人中，弹性实时环境感知是一个主要挑战，该研究引入了一种感知技术，可以检测在高速无人机飞行中常见的光照变化情况下的无人机赛门，该方法依靠轻量级神经网络骨干结构，并具备持续学习能力，实验测试表明该方法在各种困难情景下均表现出显著的鲁棒性，特别是在面对光照变化的情况下证明了其有效性。

自主无人机赛车中鲁棒门探测的持续学习

Continual Learning for Robust Gate Detection under Dynamic Lighting in  Autonomous Drone Racing

For visual object recognition tasks, the illumination variations can cause
distinct changes in object appearance and thus confuse the deep neural network
based recognition models. Especially for some rare illumination conditions,
collecting sufficient training samples could be time-consuming and expensive.
To solve this problem, in this paper we propose a novel neural network
architecture called Separating-Illumination Network (Sill-Net). Sill-Net learns
to separate illumination features from images, and then during training we
augment training samples with these separated illumination features in the
feature space. Experimental results demonstrate that our approach outperforms
current state-of-the-art methods in several object classification benchmarks.

本文提出了一种名为 Sill-Net 的神经网络架构，用于在训练过程中将光照特征从图像中分离出来，然后在特征空间中增强训练样本，并在多个目标分类基准测试中超越了当前最先进的方法。

Sill-Net：借助独立照明表示进行特征增强

Sill-Net: Feature Augmentation with Separated Illumination  Representation

Most person re-identification (ReID) approaches assume that person images are
captured under relatively similar illumination conditions. In reality,
long-term person retrieval is common, and person images are often captured
under different illumination conditions at different times across a day. In
this situation, the performances of existing ReID models often degrade
dramatically. This paper addresses the ReID problem with illumination
variations and names it as {\em Illumination-Adaptive Person Re-identification
(IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network
to dispel different scales of illuminations away while preserving individuals'
identity information. To demonstrate the illumination issue and to evaluate our
model, we construct two large-scale simulated datasets with a wide range of
illumination variations. Experimental results on the simulated datasets and
real-world images demonstrate the effectiveness of the proposed framework.

本文提出了 “IA-ReID”（Illumination-Adaptive Person Re-identification）这一针对不同照明条件下进行人物图像重识别的问题的解决方案，即采用 “IID”（Illumination-Identity Disentanglement）网络来解决不同照明下个体身份信息的丢失问题，并通过构建两个大规模模拟数据集和测试真实世界的图像来验证该方案的有效性。