Go with the Flow: Exploration and Mapping of Pedestrian Flow Patterns from Partial Observations

Abstract

Understanding how people are likely to behave in an environment is a key requirement for efficient and safe robot navigation. However, mobile platforms are subject to spatial and temporal constraints, meaning that only partial observations of human activities are typically available to a robot, while the activity patterns of people in a given environment may also change at different times. To address these issues we present as the main contribution an exploration strategy for acquiring models of pedestrian flows, which decides not only the locations to explore but also the times when to explore them. The approach is driven by the uncertainty from multiple Poisson processes built from past observations. The approach is evaluated using two long-term pedestrian datasets, comparing its performance against uninformed exploration strategies. The results show that when using the uncertainty in the exploration policy, model accuracy increases, enabling faster learning of human motion patterns.

Time-varying Pedestrian Flow Models for Service Robots

“…Similarly to the work [36] and [37], which demonstrate that incorporation of techniques to model periodic aspects of time into continuous spatial models results in powerful predictive representations, and [14], which shows the benefit of periodic temporal representations for pedestrian flow modelling, we propose to model the pedestrian flows by continuous spatio-temporal representation, which allows to model how the flows change over time.…”

“…In other words, knowledge of the typical patterns of people movement could improve socially-compliant navigation by planning robot trajectories so that robots would follow the natural flows of people, and avoid congestions and areas where they would cause a nuisance. To address this, several authors [12], [11], [13], [14] proposed models specifically aimed to represent the characteristic movement of people across the operational environment of the robot. The works mentioned above aim at the spatial aspects of pedestrian flows, i.e.…”

“…they represented the typical directions or velocities at different areas. However, the pedestrian flows themselves are not stationary, but, as shown in [15], [14], their intensities, velocities and directions strongly depend on time. A robot, capable of predicting future distributions of pedestrian flows, would be able to plan its collision-free, socially-compliant trajectories in advance, minimising the likelihood of having to alter its plan in order to avoid collisions.…”

Spatio-temporal representation for long-term anticipation of human presence in service robotics

“…A similar approach is proposed in [6], where the direction of traversal over each cell is obtained using an input-output hidden Markov model connected to the neighbouring cells. Another approach [7], [8], [9] associates each cell with a set of temporal models, which predict the direction of people movement at a particular time. The temporal models in [7], [8] are based on an approach presented in [10], which efficiently represents the periodic behaviour of changes caused by humans by employing spectral analysis.…”

“…Another approach [7], [8], [9] associates each cell with a set of temporal models, which predict the direction of people movement at a particular time. The temporal models in [7], [8] are based on an approach presented in [10], which efficiently represents the periodic behaviour of changes caused by humans by employing spectral analysis. In [10], the model is applied to occupancy grids and can predict cell occupancies for a particular time.…”