Autonomous exploration by expected information gain from probabilistic occupancy grid mapping

2016 ◽  
Author(s):  
Evan Kaufman ◽  
Taeyoung Lee ◽  
Zhuming Ai
Keyword(s):  
Information Gain ◽  
Autonomous Exploration ◽  
Occupancy Grid ◽  
Expected Information ◽  
Grid Mapping ◽  
Occupancy Grid Mapping ◽  
Expected Information Gain

scholarly journals INFORMATION-DRIVEN AUTONOMOUS EXPLORATION FOR A VISION-BASED MAV

2017 ◽  
Vol IV-2/W3 ◽  
pp. 59-66 ◽  
Author(s):  
E. Palazzolo ◽  
C. Stachniss
Keyword(s):  
Utility Function ◽  
Real Time ◽  
Information Gain ◽  
Autonomous Exploration ◽  
Elapsed Time ◽  
Aerial Vehicles ◽  
3D Environment ◽  
Starting Point ◽  
Expected Information ◽  
Expected Information Gain

Most micro aerial vehicles (MAV) are flown manually by a pilot. When it comes to autonomous exploration for MAVs equipped with cameras, we need a good exploration strategy for covering an unknown 3D environment in order to build an accurate map of the scene. In particular, the robot must select appropriate viewpoints to acquire informative measurements. In this paper, we present an approach that computes in real-time a smooth flight path with the exploration of a 3D environment using a vision-based MAV. We assume to know a bounding box of the object or building to explore and our approach iteratively computes the next best viewpoints using a utility function that considers the expected information gain of new measurements, the distance between viewpoints, and the smoothness of the flight trajectories. In addition, the algorithm takes into account the elapsed time of the exploration run to safely land the MAV at its starting point after a user specified time. We implemented our algorithm and our experiments suggest that it allows for a precise reconstruction of the 3D environment while guiding the robot smoothly through the scene.

Semantical occupancy grid mapping framework

2017 ◽  
Author(s):  
Timo Korthals ◽  
Julian Exner ◽  
Thomas Schopping ◽  
Marc Hesse
Keyword(s):  
Occupancy Grid ◽  
Grid Mapping ◽  
Occupancy Grid Mapping

scholarly journals Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

2011 ◽  
Vol 59 (11) ◽  
pp. 988-1000 ◽  
Author(s):  
Gabriele Ferri ◽  
Michael V. Jakuba ◽  
Alessio Mondini ◽  
Virgilio Mattoli ◽  
Barbara Mazzolai ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Occupancy Grid ◽  
Grid Mapping ◽  
Occupancy Grid Mapping

Occupant Body Imaging based on Occupancy Grid Mapping

2021 ◽  
Author(s):  
Takayuki Kitamura ◽  
Taro Kumagai ◽  
Takumi Takei ◽  
Isao Matsushima ◽  
Noboru Oishi ◽  
...  
Keyword(s):  
Body Imaging ◽  
Occupancy Grid ◽  
Grid Mapping ◽  
Occupancy Grid Mapping

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

Sign in / Sign up

Export Citation Format

Share Document