Non-Iterative SLAM

Abstract

The goal of this paper is to create a new framework for dense SLAM that is light enough for micro-robot systems based on depth camera and inertial sensor. Feature-based and direct methods are two mainstreams in visual SLAM. Both methods minimize photometric or reprojection error by iterative solutions, which are computationally expensive. To overcome this problem, we propose a non-iterative framework to reduce computational requirement. First, the attitude and heading reference system (AHRS) and axonometric projection are utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds can be matched in independent spaces respectively. Second, based on single key-frame training, the matching process is carried out in frequency domain by Fourier transformation, which provides a closed-form non-iterative solution. In this manner, the time complexity is reduced to $\mathcal{O}(n\log n)$, where $n$ is the number of matched points in each frame. To the best of our knowledge, this method is the first non-iterative and online trainable approach for data association in visual SLAM. Compared with the state-of-the-arts, it runs at a faster speed and obtains 3-D maps with higher resolution yet still with comparable accuracy.

This paper got the best paper award in robotic planning on 18th International Conference on Advanced Robotics (ICAR 2017)

References in corpus (1)

• High-Speed Tracking with Kernelized Correlation Filters

Cited by in corpus (11)

• Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
• Towards Real-time Semantic RGB-D SLAM in Dynamic Environments
• Ultra-Wideband Aided Fast Localization and Mapping System
• AirDOS: Dynamic SLAM benefits from Articulated Objects
• Graph Optimization Approach to Range-based Localization
• Kervolutional Neural Networks
• PyPose: A Library for Robot Learning with Physics-based Optimization
• TartanAir: A Dataset to Push the Limits of Visual SLAM
• Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
• A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras