Object SLAM
8 papers with code • 0 benchmarks • 6 datasets
SLAM (Simultaneous Localisation and Mapping) at the level of object
Benchmarks
These leaderboards are used to track progress in Object SLAM
Most implemented papers
CubeSLAM: Monocular 3D Object SLAM
Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift.
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms.
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time.
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.
Object SLAM-Based Active Mapping and Robotic Grasping
The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping.
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background.
SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints
Object SLAM introduces the concept of objects into Simultaneous Localization and Mapping (SLAM) and helps understand indoor scenes for mobile robots and object-level interactive applications.