Paper Reading AI Learner

Gravity-constrained point cloud registration

2022-03-25 17:37:44
Vladimír Kubelka, Maxime Vaidis, François Pomerleau

Abstract

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other lower-rate modalities. Moreover, in the absence of constant acceleration, the gravity vector makes two attitude angles out of three observable in the global coordinate frame. In visual odometry, this is already being used to reduce the 6-Degrees Of Freedom (DOF) pose estimation problem to 4-DOF. In lidar SLAM, the gravity measurements are often used as a penalty in the back-end global map optimization to prevent map deformations. In this work, we propose an Iterative Closest Point (ICP)-based front-end which exploits the observable DOF and provides pose estimates aligned with the gravity vector. We believe that this front-end has the potential to support the loop closure identification, thus speeding up convergences of global map optimizations. The presented approach has been extensively tested in large-scale outdoor environments as well as in the Subterranean Challenge organized by Defense Advanced Research Projects Agency (DARPA). We show that it can reduce the localization drift by 30% when compared to the standard 6-DOF ICP. Moreover, the code is readily available to the community as a part of the libpointmatcher library.

Abstract (translated)

URL

https://arxiv.org/abs/2203.13799

PDF

https://arxiv.org/pdf/2203.13799.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot