BSL: Navigation Method Considering Blind Spots Based on ROS Navigation Stack and Blind Spots Layer for Mobile Robot
Masato Kobayashi, Naoki Motoi
TL;DR
This work tackles collision risk from blind spots in autonomous mobile robot navigation by introducing a Blind Spots Layer (BSL) that leverages 3D data from RGB-D cameras within the ROS Navigation Stack. The method augments the local planning process with a velocity-aware cost function and a refined local cost map that estimates blind spots using RGB-D point clouds, replacing the conventional LRF-only approach. Key components include voxel grid filtering, Euclidean clustering, and boundary-based blind-spot position (BSBP) computation, enabling real-time, adaptive propagation of danger costs. Simulations and real-world experiments demonstrate reduced collision risk and faster goal arrival times, highlighting improved safety and efficiency for human-robot coexistence in indoor settings.
Abstract
This paper proposes a navigation method considering blind spots based on the robot operating system (ROS) navigation stack and blind spots layer (BSL) for a wheeled mobile robot. In this paper, environmental information is recognized using a laser range finder (LRF) and RGB-D cameras. Blind spots occur when corners or obstacles are present in the environment, and may lead to collisions if a human or object moves toward the robot from these blind spots. To prevent such collisions, this paper proposes a navigation method considering blind spots based on the local cost map layer of the BSL for the wheeled mobile robot. Blind spots are estimated by utilizing environmental data collected through RGB-D cameras. The navigation method that takes these blind spots into account is achieved through the implementation of the BSL and a local path planning method that employs an enhanced cost function of dynamic window approach. The effectiveness of the proposed method was further demonstrated through simulations and experiments.