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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand 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Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment RobotPan: A 360$^\circ$ Surround-View Robotic Vision System for Embodied Perception Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses 4th Workshop on Maritime 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Reinforcement Learning Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring 3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding Action Images: End-to-End Policy Learning via Multiview Video Generation Towards Generalizable Robotic Manipulation in Dynamic Environments General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters IROSA: Interactive Robot Skill Adaptation using Natural Language Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation From Instruction to Event: Sound-Triggered Mobile Manipulation Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODACER) for Safe Reinforcement Learning in Optimal Control Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets? Robust Verification of Controllers under State Uncertainty via Hamilton-Jacobi Reachability Analysis Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion Volumetric Ergodic Control RoboTAG: End-to-end Robot Configuration Estimation via Topological Alignment Graph TwinOR: Photorealistic Digital Twins of Dynamic Operating Rooms for Embodied AI Research Multimodal Diffusion Forcing for Forceful Manipulation X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance Multi-Modal Manipulation via Multi-Modal Policy Consensus AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving Constrained Decoding for Safe Robot Navigation Foundation Models FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving LLM-based Realistic Safety-Critical Driving Video Generation Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents Learning to Play Piano in the Real World Scalable Unseen Objects 6-DoF Absolute Pose Estimation with Robotic Integration Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI Convex Hulls of Reachable Sets
MEKF Ignoring Initial Conditions for Attitude Estimation Using Vector Observations
Lubin Chang · 2021-10-26 · via cs.RO updates on arXiv.org

In this paper, the well-known multiplicative extended Kalman filter (MEKF) is re-investigated for attitude estimation using vector observations. From the Lie group theory, it is shown that the attitude estimation model is group affine and its error state model should be trajectory-independent. Moreover, with such trajectory-independent error state model, the linear Kalman filter is still effective for large initialization errors. However, the measurement model of the traditional MEKF is dependent on the attitude prediction, which is therefore trajectory-dependent. This is also the main reason why the performance of traditional MEKF is degraded for large initialization errors. Through substitution of the attitude prediction related term with the vector observation in body frame, a trajectory-independent measurement model is derived for MEKF. Meanwhile, the MEKFs with reference attitude error definition and with global state formulating on special Euclidean group have also been studied, with main focus on derivation of the trajectory-independent measurement models. Extensive Monte Carlo simulations and field test of attitude estimation implementations demonstrate that the performance of MEKFs can be much improved with trajectory-independent measurement models.

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