A featureless approach for object detection and tracking in dynamic environments

One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

Automated summary

In this paper, a ROS-based efficient algorithm for constructing dynamic maps is proposed, which utilizes spatial-temporal locality for detecting and tracking moving objects without prior knowledge of their geometrical features. The algorithm efficiently decodes sensory data to estimate the time-varying object boundary and updates the dynamic environment through manipulating spatial-temporal locality, achieving lower time-complexity. The algorithm is validated through simulations and experiments, demonstrating accurate detection and tracking of objects under low sensor noise and acceptable speed limits.