Triangle Splatting SLAM performs online tracking and mapping via differentiable rendering of triangle soups in RGB-D data, converts to connected meshes on-the-fly, and reports better 3D geometry than baselines on Replica and TUM-RGBD while matching tracking accuracy.
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A micro lie theory for state estimation in robotics
27 Pith papers cite this work. Polarity classification is still indexing.
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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 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.
G-solver combines Gaussian Belief Propagation and Gaussian Process priors into a fully Gaussian distributed solver for continuous-time SLAM that handles heterogeneous asynchronous sensors and multi-camera setups without explicit synchronization.
Self-supervised contact detection for legged robot odometry using joint encoders outperforms supervised methods that require force sensors.
A pixel-distributed direct visual odometry and depth estimation method using Gaussian Belief Propagation with keyframe anchoring for on-sensor consensus.
SFMDS parametrizes dynamical systems via flow matching with soft penalty or hard architectural constraints to enforce stability while preserving multimodality, extended to Lie groups.
Lie Diffuser Actor corrects Euclidean representation of SE(3) poses in diffusion VLA policies via tangent-space score matching on left-invariant SDEs with exponential retraction, yielding +7.3% task length on CALVIN ABC→D.
Proves ||exp(theta)||_op <= 1 + ||theta||_F on se(3) and constructs J* with L_J*(R; se(3)) >= 0.0505 R^2 for R >= 2, showing intermediate quadratic growth.
LAST linearizes action manifolds with Lie-algebraic mapping and discretizes them into approximately isotropic charts to align with VL semantic geometry via Gromov-Wasserstein distance.
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.
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.
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Operator-norm bounds and a quadratic lower-growth example for the special Euclidean algebra se(3)
Proves ||exp(theta)||_op <= 1 + ||theta||_F on se(3) and constructs J* with L_J*(R; se(3)) >= 0.0505 R^2 for R >= 2, showing intermediate quadratic growth.