Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2

• URL: http://arxiv.org/abs/2210.00278v1
• Date: Sat, 1 Oct 2022 13:25:11 GMT
• Title: Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
• Authors: Ali Eslamian, Mohammad R. Ahmadzadeh
• Abstract summary: This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system. Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several decades. However, there are still unresolved issues, such as how to deal with moving objects in dynamic situations. Classic SLAM systems depend on the assumption of a static environment, which becomes unworkable in highly dynamic situations. Several methods have been presented to tackle this issue in recent years, but each has its limitations. This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs depth information and semantic segmentation to identify and eradicate dynamic spots to accomplish semantic SLAM for dynamic situations. Evaluation of public TUM datasets indicates that Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.

Related papers

• Learning System Dynamics without Forgetting [60.08612207170659]
  Predicting trajectories of systems with unknown dynamics is crucial in various research fields, including physics and biology. We present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics. We construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics.
  arXiv  Detail & Related papers  (2024-06-30T14:55:18Z)
• DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM [5.267859554944985]
  We introduce DDN-SLAM, the first real-time dense dynamic neural implicit SLAM system integrating semantic features. Compared to existing neural implicit SLAM systems, the tracking results on dynamic datasets indicate an average 90% improvement in Average Trajectory Error (ATE) accuracy.
  arXiv  Detail & Related papers  (2024-01-03T05:42:17Z)
• Event-based Simultaneous Localization and Mapping: A Comprehensive Survey [52.73728442921428]
  Review of event-based vSLAM algorithms that exploit the benefits of asynchronous and irregular event streams for localization and mapping tasks. Paper categorizes event-based vSLAM methods into four main categories: feature-based, direct, motion-compensation, and deep learning methods.
  arXiv  Detail & Related papers  (2023-04-19T16:21:14Z)
• Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios [70.70421502784598]
  RDS-SLAM can build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios.
  arXiv  Detail & Related papers  (2022-10-10T11:03:32Z)
• MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation [5.33931801679129]
  MOTSLAM is a dynamic visual SLAM system with the monocular configuration that tracks both poses and bounding boxes of dynamic objects. Our experiments on the KITTI dataset demonstrate that our system has reached best performance on both camera ego-motion and object tracking on monocular dynamic SLAM.
  arXiv  Detail & Related papers  (2022-10-05T06:07:10Z)
• TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM [0.0]
  TwistSLAM++ is a semantic, dynamic, SLAM system that fuses stereo images and LiDAR information. We show on classical benchmarks that this fusion approach based on multimodal information improves the accuracy of object tracking.
  arXiv  Detail & Related papers  (2022-09-16T12:28:21Z)
• D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance [61.14088096348959]
  We introduce a self-supervised deep SLAM method that robustly operates in dynamic scenes while accurately identifying dynamic components. We propose a dynamic update module based on this representation and develop a dense SLAM system that excels in dynamic scenarios.
  arXiv  Detail & Related papers  (2022-07-18T17:47:39Z)
• NICE-SLAM: Neural Implicit Scalable Encoding for SLAM [112.6093688226293]
  NICE-SLAM is a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust.
  arXiv  Detail & Related papers  (2021-12-22T18:45:44Z)
• Learning to Segment Dynamic Objects using SLAM Outliers [5.4310785842119795]
  We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network.
  arXiv  Detail & Related papers  (2020-11-12T08:36:54Z)
• DOT: Dynamic Object Tracking for Visual SLAM [83.69544718120167]
  DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.
  arXiv  Detail & Related papers  (2020-09-30T18:36:28Z)
• DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localization [5.198840934055703]
  DynamicSLAM is an indoor localization technique that eliminates the need for the daunting calibration step. We employ the phone inertial sensors to keep track of the user's path. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment.
  arXiv  Detail & Related papers  (2020-03-30T19:49:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.