Search Results for author:
Found 7 papers, 4 papers with code
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching.
BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap
Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes.
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.
Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment
To solve this problem, we propose a quadric initialization method based on the decoupling of the quadric parameters method, which improves the robustness to observation noise.
Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry
In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane.
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.
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.
