Map building with mobile robots in dynamic environments

By Dirk Hähnel, Rudolph Triebel, Wolfram Burgard, Sebastian Thrun. IEEE International Conference on Robotics and Automation, 2003

306 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9bb704b4194591cd33032d9631044fa47fe0ab4c

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Robust Multiperson Tracking from a Mobile Platform (2009, background): “In robotics, various approaches for SLAM in dynamic environments exist [5], [22], [55], related to the above, but mostly focusing on range data.”
Detection, classification and tracking of moving objects in a 3D environment (2012, methodology): “Most of these use the grid maps, specially a well-established mapping technique of occupancy grid algorithm [2], to represent the environment.”
Robust monocular SLAM in dynamic environments (2013, methodology): “[14] use Expectation-Maximization (EM) algorithm to update the probabilistic estimate about which measurement (obtained with a laserrange scanner) might correspond to a static/dynamic object.”
A mobile vision system for robust multi-person tracking (2008, background): “In robotics, various approaches for SLAM in dynamic environments [3] [11] exist , related to the above, but mostly focusing on range data.”
Learning Occupancy Grid Maps with Forward Sensor Models (2003, unknown): “In a recent article (H¨ahnel et al., 2002), we extended the basic EM approach to accommodate pose uncertainty and environment dynamics.”
A multilevel relaxation algorithm for simultaneous localization and mapping (2005, background): “First, some approaches attempt to explicitly discriminate “dynamic” from “static” aspects [3], [10], [11], [6].”
HERB: a home exploring robotic butler (2010, background): “However, these approaches either use a static map, or seek to remove moving objects from the map (Schulz et al. 2003; Hahnel et al. 2003).”
Model-free detection and tracking of dynamic objects with 2D lidar (2015, background): “Finally, the work by Hahnel et al. (2003) is also relevant where the authors formulate an EM algorithm to solve for a set of hidden indicator variables for each laser point to determine whether it is static or dynamic.”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “It is called the revisiting problem (Stewart et al., 2003 Thrun and Liu, 2003 Hähnel et al., 2003).”
Using Boosted Features for the Detection of People in 2D Range Data (2007, background): “Hähnel et al. [8] have considered the problem of identifying beams in range scans that are reflected by dynamic objects.”
Localization in highly dynamic environments using dual-timescale NDT-MCL (2014, methodology): “al [10] use an Expectation Maximization algorithm to classify the outliers from the complete dataset.”