Introduction• A novel method for robot spatio-temporal exploration for long-

term scenarios.• Aimed at obtaining the environment model the world and

learning about the dynamics.• Crucial for long-term deployment of mobile robots in human-

populated spaces.

References[1] Krajník et al.: Long-term topological localisation for servicerobots in dynamic environments using spectral maps. In IROS `14.[2] ] Krajník et al.: FROctomap: an efficient spatio-temporalenvironment representation. In TAROS `14.[3] Krajník et al.: Spectral analysis for long-term mapping. InICRA`14.[4] Amigoni, Francesco, and Vincenzo Caglioti. "An information-based exploration strategy for environment mapping with mobilerobots." Robotics and Autonomous Systems 58.5 (2010): 684-699.

FROctoMapThe efficiency of OctoMap to model large spatial scales and theefficiency of FREMen to represent long periods of time arecombined in a novel spatio-temporal environment model [2]:

• FreMEn considers the probability of each environment state asa function of time p(t).

• The function p(t) is represented by the most prominentcomponents of its frequency spectrum P(ω).

• Efficient Octree-based probabilistic representation of large-scalethree-dimensional environments [3].

• OctoMap locally adapts the occupancy grid resolution to the levelof detail required [3].

http://robots.lincoln.ac.ukLincoln Centre for Autonomous Systems

The research leading to these results hasreceived funding from the EuropeanCommunity’s Seventh Framework Programmeunder grant agreement No. 600623, STRANDS.

Motivation• Most of the environment models used in the mobile robotics

domain have been tailored to represent large, but static scenes.• Neglecting the natural environment dynamics decreases efficiency

of the planning, localization and navigation tasks.• Incorporating dynamics into the robot’s environment model

improves its ability to reliably operate for long periods of time [1].

FROctoMapOctoMap FreMEn

Observed occupancy s(t) of each Octomap voxel is represented by its frequency spectrum

P(ω) and an outlier set O:

Allows to retrieve or predict the environment states for a given time.

April 7th 11:00am (retrieved)

April 7th 11:00pm (predicted)

Next Best View Strategies• The selection of the next location to visit is information-driven.• Information gain is estimated by expected entropy reduction.• Environment is static making the exploration task finite.

Spatio-temporal Exploration• Environment dynamics causes the exploration to be continuous.• The FrOctomap efficiently represents environment dynamics.• It allows to estimate the information gained by visiting a

particular location at a particular time.• Exploration strategies must consider the temporal domain.• Besides the exploration, the robot must preform other tasks,

which leads to Exploitation vs Exploration dillema.

Static environment example in MORSE simulator.

Partially explored environment using a Next Best View strategie represented by an occupancy grid.