ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments

Abstract

We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more. The data is collected in photo-realistic simulation environments with the presence of environmental non-targeted variations, such as air turbidity and light condition changes, as well as targeted object changes in industrial indoor environments. By collecting data in simulations, multi-modal sensor data and precise ground truth labels are obtainable such as the RGB image, depth image, semantic segmentation, change segmentation, camera poses, and 3D reconstructions. While the previous online SCD datasets evaluate models given well-aligned image pairs, ChangeSim also provides raw unpaired sequences that present an opportunity to develop an online SCD model in an end-to-end manner, considering both pairing and detection. Experiments show that even the latest pair-based SCD models suffer from the bottleneck of the pairing process, and it gets worse when the environment contains the non-targeted variations. Our dataset is available at https://sammica.github.io/ChangeSim/.

Sparse Image based Navigation Architecture to Mitigate the need of precise Localization in Mobile Robots

“…It is well known that most of the industrial use case for mobile robots using SLAM pipelines have utility in warehouse scenarios [40]. The challenging and changing industrial environments have been well documented in the literature and challenging datasets like the Scene Change Detection dataset are made available to tackle [23]. Due to the existence of featureless walls and rapidly changing scenarios, exact localization based methods fail.…”