CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter
7 Pith papers cite this work. Polarity classification is still indexing.
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FU-MPC is a receding-horizon MPC controller that treats motorized LiDAR rotation as an explicit variable to jointly maximize frontier exploration utility and minimize direction-dependent localization uncertainty during UAV flight.
A real-time underwater SLAM system uses reliability-aware multi-sensor fusion and quadtree-guided 3D Gaussian mapping to maintain localization and photorealistic reconstruction during visual degradation.
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.
The NANO filter uses natural gradient descent to iteratively refine Gaussian state estimates while preserving covariance positive definiteness and exactly recovering the Kalman update in the linear-Gaussian case.
Local normal-vector constraints plus degeneracy-guided map updates cut solid-state LiDAR-inertial odometry RMSE by up to 12.8% in extreme, geometrically degenerate environments.
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.
citing papers explorer
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CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping
CT-VoxelMap achieves more accurate and efficient continuous-time LiDAR-inertial odometry by estimating control point increments on Lie groups, using IMU data to correct B-spline fitting errors online, and managing a probabilistic adaptive voxel map with a re-estimation policy.
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FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR
FU-MPC is a receding-horizon MPC controller that treats motorized LiDAR rotation as an explicit variable to jointly maximize frontier exploration utility and minimize direction-dependent localization uncertainty during UAV flight.
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APVI-SLAM: Real-Time Acoustic-Pressure-Visual-Inertial Localization and Photorealistic Mapping System in Complex Underwater Environment
A real-time underwater SLAM system uses reliability-aware multi-sensor fusion and quadtree-guided 3D Gaussian mapping to maintain localization and photorealistic reconstruction during visual degradation.
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Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.
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Natural Gradient Bayesian Filtering: Geometry-Aware Filter for Dynamical Systems
The NANO filter uses natural gradient descent to iteratively refine Gaussian state estimates while preserving covariance positive definiteness and exactly recovering the Kalman update in the linear-Gaussian case.
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Environment-Adaptive Solid-State LiDAR-Inertial Odometry
Local normal-vector constraints plus degeneracy-guided map updates cut solid-state LiDAR-inertial odometry RMSE by up to 12.8% in extreme, geometrically degenerate environments.
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MfNeuPAN: Proactive End-to-End Navigation in Dynamic Environments via Direct Multi-Frame Point Constraints
MfNeuPAN uses multi-frame observations and predicted future obstacle paths to enable proactive end-to-end robot navigation that improves robustness in unknown dynamic environments.