Importance sampling

Monte Carlo localization for mobile robots

Frank Dellaert, D. Fox, Wolfram Burgard, Sebastian Thrun

Citations: 1613 • 2003

KLD-Sampling: Adaptive Particle Filters

Dieter Fox

Citations: 249 • 2002

Monte Carlo Localization with Mixture Proposal Distribution

Sebastian Thrun, Dieter Fox, Wolfram Burgard

Citations: 190 • 2000

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Randal Douc, Nando de Freitas, Kevin J. Murphy, Stuart Russell

Citations: 145 • 2013

Cross-entropy motion planning

Marin Kobilarov

Citations: 137 • 2012

Estimating probability of collision for safe motion planning under Gaussian motion and sensing uncertainty

Sachin Patil, Jur van den Berg, Ron Alterovitz

Citations: 114 • 2012

Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy

David Hsu, Gildardo Sanchez‐Ante, Zheng Sun

Citations: 111 • 2006

Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty

Lucas Janson, Edward Schmerling, Marco Pavone

Citations: 105 • 2017

Adaptive Importance Sampling for Control and Inference

Hilbert J. Kappen, H. C. Ruiz

Citations: 90 • 2016

Learning Implicit Sampling Distributions for Motion Planning

Clark Zhang, Jinwook Huh, Daniel D. Lee

Citations: 67 • 2018

Optimal Filtering for Non-parametric Observation Models: Applications to Localization and SLAM

Jose‐Luis Blanco, Javier González, Juan‐Antonio Fernández‐Madrigal

Citations: 48 • 2010

Bayesian Local Sampling-Based Planning

Tin Lai, Philippe Morere, Fábio Ramos, Gilad Francis

Citations: 43 • 2020

Analyzing gaussian proposal distributions for mapping with rao-blackwellized particle filters

Cyrill Stachniss, Giorgio Grisetti, Wolfram Burgard, Nicholas Roy

Citations: 41 • 2007

Safety assessment of trajectories for navigation in uncertain and dynamic environments

Daniel Althoff, Dirk Wollherr, Martin Buss

Citations: 28 • 2011

Reliable Monte Carlo localization for mobile robots

Naoki Akai

Citations: 27 • 2023

Hybrid Localization using Model- and Learning-Based Methods: Fusion of Monte Carlo and E2E Localizations via Importance Sampling

Naoki Akai, Takatsugu Hirayama, Hiroshi Murase

Citations: 25 • 2020

Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers

Elia Trevisan, Javier Alonso–Mora

Citations: 25 • 2024

Safe and Efficient Dynamic Window Approach for Differential Mobile Robots With Stochastic Dynamics Using Deterministic Sampling

Shinya Yasuda, Taichi Kumagai, Hiroshi Yoshida

Citations: 23 • 2023

Learning an Optimal Sampling Distribution for Efficient Motion Planning

Richard Cheng, Krishna Shankar, Joel W. Burdick

Citations: 19 • 2020

Kernel density estimation based self-learning sampling strategy for motion planning of repetitive tasks

Thomas Fridolin Iversen, Lars‐Peter Ellekilde

Citations: 15 • 2016