eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform
Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy efficient architecture for real-time ORB (Oriented-FAST and Rotated- BRIEF) based visual SLAM system by accelerating the most time consuming stages of feature extraction and matching on FPGA platform.Moreover, the original ORB descriptor pattern is reformed as a rotational symmetric manner which is much more hardware friendly. Optimizations including rescheduling and parallelizing are further utilized to improve the throughput and reduce the memory footprint. Compared with Intel i7 and ARM Cortex-A9 CPUs on TUM dataset, our FPGA realization achieves up to 3X and 31X frame rate improvement, as well as up to 71X and 25X energy efficiency improvement, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous NavigationSimultaneous Localization and MappingSimilar Papers 제목 키워드 기반
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstru…
3D ReconstructionCamera LocalizationNeRFSimultaneous Localization and MappingFeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time
We present a real-time tracking SLAM system that unifies efficient camera tracking with photorealistic feature-enriched mapping using 3D Gaussian Splatting (3DGS). Our main contribution is integrating dense feature raste…
Semantic SLAMNeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes im…
3D ReconstructionDenoisingDepth CompletionDepth Estimation+2Loop closure detection using local 3D deep descriptors
We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted f…
Loop Closure DetectionLRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantage…
Autonomous DrivingMixed RealitySimultaneous Localization and MappingTensor Decomposition