ArticleSensors (Basel, Switzerland)2024
YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Lightweight 2.5D SLAM with Dynamic Map Refinement and Height-Aware Encoding for Resource-Constrained Indoor Robots.Sensors (Basel, Switzerland) · 2026Article
- An instance-aware segmentation and optical flow based DVI-SLAM for dynamic environments.Scientific reports · 2026Article
- RTS-SLAM: A Trajectory Consistency-Driven Multi-Constraint Dynamic Feature-Rejection Method for Visual SLAM in Dynamic Environments.Sensors (Basel, Switzerland) · 2026Article
- Dynamic SLAM by Combining Rigid Feature Point Set Modeling and YOLO.Sensors (Basel, Switzerland) · 2025Article
- SGDO-SLAM: A Semantic RGB-D SLAM System with Coarse-to-Fine Dynamic Rejection and Static Weighted Optimization.Sensors (Basel, Switzerland) · 2025Article
- A Multi-Strategy Visual SLAM System for Motion Blur Handling in Indoor Dynamic Environments.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.