Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
Lukas Schmid, Olov Andersson, Aurelio Sulser, Patrick Pfreundschuh,, and Roland Siegwart

TL;DR
Dynablox is a real-time, online mapping-based method for detecting diverse moving objects in complex environments, robustly handling various object types and scene complexities without relying on appearance assumptions.
Contribution
It introduces a novel free-space estimation approach that enables robust moving object detection in complex scenes without appearance-based assumptions.
Findings
Achieves 86% IoU at 17 FPS in real-world robotic settings.
Outperforms recent appearance-based classifiers.
Approaches offline method performance and generalizes to complex environments.
Abstract
Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose Dynablox, a novel online mapping-based approach for robust moving object detection in complex environments. The central idea of our approach is to incrementally estimate high confidence free-space areas by modeling and accounting for sensing, state estimation, and mapping limitations during online robot operation. The spatio-temporally conservative free space estimate enables robust detection of moving objects without making any assumptions on the appearance of objects or environments. This allows deployment in complex scenes such as multi-storied buildings or staircases, and for diverse moving objects such as people carrying various items, doors swinging or even balls rolling around. We thoroughly evaluate our approach on real-world data sets,…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Advanced Image and Video Retrieval Techniques
