Adaptive Cost Function for Pointcloud Registration
In this paper we introduce an adaptive cost function for pointcloud registration. The algorithm automatically estimates the sensor noise, which is important for generalization across different sensors and environments. Through experiments on real and synthetic data, we show significant improvements in accuracy and robustness over state-of-the-art solutions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Robust Feature-Based Point Registration Using Directional Mixture Model
This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i.e. rotation matrix and translation vector) between two pointcloud dataset. The method improves the robustnes…
TranslationSEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of …
Graph AttentionGraph Neural NetworkInductive BiasPose EstimationSuper 4PCS Fast Global Pointcloud Registration via Smart Indexing
Data acquisition in large‐scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), …
Point Cloud RegistrationA Termination Criterion for Probabilistic PointClouds Registration
Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state of the art algorithms for local point clouds registration. However, its performances have been teste…
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs
With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration methods working with unstructured pointclouds…
Keypoint Detection