Rostkowska Marta, Skrzypczyński Piotr
Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland.
Sensors (Basel). 2023 Jul 18;23(14):6485. doi: 10.3390/s23146485.
This paper considers the task of appearance-based localization: visual place recognition from omnidirectional images obtained from catadioptric cameras. The focus is on designing an efficient neural network architecture that accurately and reliably recognizes indoor scenes on distorted images from a catadioptric camera, even in self-similar environments with few discernible features. As the target application is the global localization of a low-cost service mobile robot, the proposed solutions are optimized toward being small-footprint models that provide real-time inference on edge devices, such as Nvidia Jetson. We compare several design choices for the neural network-based architecture of the localization system and then demonstrate that the best results are achieved with embeddings (global descriptors) yielded by exploiting transfer learning and fine tuning on a limited number of catadioptric images. We test our solutions on two small-scale datasets collected using different catadioptric cameras in the same office building. Next, we compare the performance of our system to state-of-the-art visual place recognition systems on the publicly available COLD Freiburg and Saarbrücken datasets that contain images collected under different lighting conditions. Our system compares favourably to the competitors both in terms of the accuracy of place recognition and the inference time, providing a cost- and energy-efficient means of appearance-based localization for an indoor service robot.
从折反射相机获取的全向图像中进行视觉场所识别。重点在于设计一种高效的神经网络架构，该架构能够在折反射相机拍摄的失真图像上准确且可靠地识别室内场景，即使是在几乎没有可辨别特征的自相似环境中。由于目标应用是低成本服务移动机器人的全局定位，因此所提出的解决方案朝着小尺寸模型进行了优化，这些模型能够在诸如英伟达Jetson等边缘设备上提供实时推理。我们比较了定位系统基于神经网络的架构的几种设计选择，然后证明通过利用迁移学习并在有限数量的折反射图像上进行微调所产生的嵌入（全局描述符）能够取得最佳结果。我们在同一办公楼内使用不同折反射相机收集的两个小规模数据集上测试了我们的解决方案。接下来，我们将我们系统的性能与公开可用的COLD弗莱堡和萨尔布吕肯数据集上的最先进视觉场所识别系统进行比较，这些数据集包含在不同光照条件下收集的图像。我们的系统在场所识别准确性和推理时间方面均优于竞争对手，为室内服务机器人提供了一种经济高效的基于外观的定位方法。
