KiTe augments sampling-based kinodynamic planning with terminal costs in belief space, proving asymptotic optimality preservation and improved goal-reaching probability bounds via Wasserstein minimization, supported by learned uncertainty models and experiments.
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A micro lie theory for state estimation in robotics
19 Pith papers cite this work. Polarity classification is still indexing.
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ReActor jointly optimizes motion retargeting and RL policy training with an approximate gradient to generate physically consistent robot motions from human references using only sparse body correspondences.
Lie-group recursive dynamics algorithms are extended to higher-order time derivatives for floating-base robots, with quadratic computational scaling shown versus exponential for automatic differentiation on a 12-DoF aerial manipulator.
A factor graph framework estimates continuum robot shapes using Geometric Variable Strain coefficients constrained by Magnus expansion kinematics, achieving mean position errors below 2 mm in simulation.
GaussianFlow SLAM aligns projected Gaussian motion with optical flow to regularize monocular 3D Gaussian splatting SLAM, yielding better map quality and pose accuracy than prior methods.
Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
OCELOT fuses a debounced force GMM-FSM and kinematic GLRT into an ESEKF to produce accurate leg odometry from IMU, encoders and force sensors while explicitly detecting and rejecting slippage on diverse terrains.
FUSE introduces a unified interface for state estimation in SLAM that separates key design choices, with a LiDAR-IMU example showing 1.626m error on a 418m sequence, 7.9% better than Faster-LIO.
Hybrid analytical/AD method for SE(3) NLLs delivers exact Hessians 5x faster than finite-differencing while matching nested AD to machine precision, plus a fix for origin NaNs in the scalar basis.
Factor graphs and Chebyshev polynomials enable robust continuous-time state and trajectory estimation for tensegrity robots by fusing RGB-D camera and cable sensor data.
SCC-Loc achieves 9.37 m mean localization error for UAV thermal images against satellite references, a 7.6-fold gain inside the 5 m threshold over prior methods, using a shared DINOv2 backbone plus three new semantic-consensus modules.
A sensor-agnostic LiDAR-inertial odometry method using simplified IMU integration and novel regularization on scan-to-map registration that maintains consistent performance across diverse sensors and platforms.
Introduces an eager-mode PyTorch BA library with GPU-accelerated sparse ops claiming 18.5-23x speedups over GTSAM, g2o, and Ceres.
A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
An IterIEKF algorithm for quadruped odometry, relying on proprioceptive kinematic constraints, outperforms vanilla IEKF and SO(3) Kalman filters in accuracy and consistency on simulations and real datasets.
BIEVR-LIO improves robustness of LiDAR-inertial odometry by representing maps as voxel-wise oriented height images and sampling points only from geometrically informative regions.
A hybrid offline-online approach adapts neural robot dynamics models via low-rank second-order updates to enable robust predictive tracking control on quadrotors in novel conditions.
A score-based method is introduced to guide optimization in geometric view diffusion models toward correct viewpoints, improving convergence and sample efficiency over naive multistart strategies.
A MAP-based joint calibration method for magnetometer-IMU pairs achieves 20-30% lower RMSE in parameters than two state-of-the-art methods, calibrates 30 pairs in under two minutes, and supports comparable navigation performance.
citing papers explorer
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Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
KiTe augments sampling-based kinodynamic planning with terminal costs in belief space, proving asymptotic optimality preservation and improved goal-reaching probability bounds via Wasserstein minimization, supported by learned uncertainty models and experiments.
-
ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
ReActor jointly optimizes motion retargeting and RL policy training with an approximate gradient to generate physically consistent robot motions from human references using only sparse body correspondences.
-
Lie Group Formulation of Recursive Dynamics Algorithms of Higher Order for Floating-Base Robots
Lie-group recursive dynamics algorithms are extended to higher-order time derivatives for floating-base robots, with quadratic computational scaling shown versus exponential for automatic differentiation on a 12-DoF aerial manipulator.
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Factor Graph-Based Shape Estimation for Continuum Robots via Magnus Expansion
A factor graph framework estimates continuum robot shapes using Geometric Variable Strain coefficients constrained by Magnus expansion kinematics, achieving mean position errors below 2 mm in simulation.
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GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow
GaussianFlow SLAM aligns projected Gaussian motion with optical flow to regularize monocular 3D Gaussian splatting SLAM, yielding better map quality and pose accuracy than prior methods.
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Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints
Complementarity constraints are treated as a Lie group under relaxation to enable parameterization that satisfies them by construction in LCQP solvers.
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OCELOT: Odometry and Contact Estimation for Legged Robots
OCELOT fuses a debounced force GMM-FSM and kinematic GLRT into an ESEKF to produce accurate leg odometry from IMU, encoders and force sensors while explicitly detecting and rejecting slippage on diverse terrains.
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FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
FUSE introduces a unified interface for state estimation in SLAM that separates key design choices, with a LiDAR-IMU example showing 1.626m error on a 418m sequence, 7.9% better than Faster-LIO.
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Exact Higher-Order Derivatives for SE(3) via Analytical/AD Methods
Hybrid analytical/AD method for SE(3) NLLs delivers exact Hessians 5x faster than finite-differencing while matching nested AD to machine precision, plus a fix for origin NaNs in the scalar basis.
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State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials
Factor graphs and Chebyshev polynomials enable robust continuous-time state and trajectory estimation for tensegrity robots by fusing RGB-D camera and cable sensor data.
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SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization
SCC-Loc achieves 9.37 m mean localization error for UAV thermal images against satellite references, a 7.6-fold gain inside the 5 m threshold over prior methods, using a shared DINOv2 backbone plus three new semantic-consensus modules.
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A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
A sensor-agnostic LiDAR-inertial odometry method using simplified IMU integration and novel regularization on scan-to-map registration that maintains consistent performance across diverse sensors and platforms.
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Bundle Adjustment in the Eager Mode
Introduces an eager-mode PyTorch BA library with GPU-accelerated sparse ops claiming 18.5-23x speedups over GTSAM, g2o, and Ceres.
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Temporally Consistent Object 6D Pose Estimation for Robot Control
A factor graph that fuses motion models with uncertainty-aware pose measurements improves temporal consistency and benchmark scores for vision-based robot control.
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Iterated Invariant EKF for Quadruped Robot Odometry
An IterIEKF algorithm for quadruped odometry, relying on proprioceptive kinematic constraints, outperforms vanilla IEKF and SO(3) Kalman filters in accuracy and consistency on simulations and real datasets.
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BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps
BIEVR-LIO improves robustness of LiDAR-inertial odometry by representing maps as voxel-wise oriented height images and sampling points only from geometrically informative regions.
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Adapting Neural Robot Dynamics on the Fly for Predictive Control
A hybrid offline-online approach adapts neural robot dynamics models via low-rank second-order updates to enable robust predictive tracking control on quadrotors in novel conditions.
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Landscape-Awareness for Geometric View Diffusion Model
A score-based method is introduced to guide optimization in geometric view diffusion models toward correct viewpoints, improving convergence and sample efficiency over naive multistart strategies.
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Joint Magnetometer-IMU Calibration via Maximum A Posteriori Estimation
A MAP-based joint calibration method for magnetometer-IMU pairs achieves 20-30% lower RMSE in parameters than two state-of-the-art methods, calibrates 30 pairs in under two minutes, and supports comparable navigation performance.