5 accepted papers
Numerous deep learning-based works focusing on 3D semantic segmentation have been proposed and have achieved impressive performance. However, due to the catastrophic forgetting, existing methods will degrade dramatically in a real-world scenario where new 3D semantic categories are arriving continua…
In this paper, we propose rWiFiSLAM, an indoor localisation system based on WiFi ranging measurements. Indoor localisation techniques play an important role in mobile robots when they cannot access good quality GPS signals in indoor environments. Indoor localisation also has many other applications,
Crowd counting has a wide range of applications such as video surveillance and public safety. Many existing methods only focus on improving the accuracy of counting but ignore the importance of density maps. It's no doubt that a high-quality density map contains more information such as localization…
In this paper, we propose SolarSLAM, a batteryfree loop closure method for indoor localisation. Inertial Measurement Unit (IMU) based indoor localisation method has been widely used due to its ubiquity in mobile devices, such as mobile phones, smartwatches and wearable bands. However, it suffers fro…
Localisation is of importance for many applications. Our motivating scenarios are short-term construction work and emergency rescue. Not only is accuracy necessary, these scenarios also require rapid setup and robustness to environmental conditions. These requirements preclude the use of many tradit…