Work-in-Progress: Smart data reduction in SLAM methods for embedded systems

Abstract

The 3D reconstruction of simultaneous localization and mapping (SLAM) is an important topic in the field for transport systems such as drones, service robots and mobile AR/VR devices. Compared to a point cloud representation, the 3D reconstruction based on meshes and voxels is particularly useful for high-level functions, like obstacle avoidance or interaction with the physical environment. This article reviews the implementation of a visual-based 3D scene reconstruction pipeline on resource-constrained hardware platforms. Real-time performances, memory management and low power consumption are critical for embedded systems. A conventional SLAM pipeline from sensors to 3D reconstruction is described, including the potential use of deep learning. The implementation of advanced functions with limited resources is detailed. Recent systems propose the embedded implementation of 3D reconstruction methods with different granularities. The trade-off between required accuracy and resource consumption for real-time localization and reconstruction is one of the open research questions identified and discussed in this paper.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…ROVIO-SLAM workflow is divided into three stages ( Picard et al, 2023 ; Nguyen et al, 2020 ; Schneider et al, 2018 ). First, data from visual cameras and IMU are obtained and prepared for processing.…”

“…It involves the execution of various processes that are crucial for successfully determining where the robot is. Feature tracking plays a central role during this phase, with a primary focus on tasks such as feature extraction, matching, re-localization, and pose estimation ( Picard et al, 2023 ). It aims to align and identify the frames that guide the estimation and creation of the initial keyframe for the input data ( Ai et al, 2021 ).…”

“…The roots of SLAM can be traced back to nearly three decades ago, when it was first introduced by Smith et al Picard et al (2023) ; Khoyani and Amini (2023) . Recently, visual SLAM has changed a lot and made a big impact on robotics and computer vision ( Khoyani and Amini, 2023 ).…”