Article
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Preserved in Portico This version is not peer-reviewed
Shaped-based Tightly Coupled IMU/Camera Object-level SLAM
Version 1
: Received: 2 August 2023 / Approved: 3 August 2023 / Online: 3 August 2023 (10:33:25 CEST)
A peer-reviewed article of this Preprint also exists.
Asl Sabbaghian Hokmabadi, I.; Ai, M.; El-Sheimy, N. Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM. Sensors 2023, 23, 7958. Asl Sabbaghian Hokmabadi, I.; Ai, M.; El-Sheimy, N. Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM. Sensors 2023, 23, 7958.
Abstract
Object-level Simultaneous Localization and Mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an Inertial Measurement Unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object’s surface and not the object’s shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.
Keywords
object‐level SLAM; RBPF‐SLAM; shape‐based pose estimation; undelayed initialization; IMU/camera fusion; tightly coupled; coarse‐to‐fine pose estimation
Subject
Engineering, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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