SCOPE: Stochastic Cartographic Occupancy
Prediction Engine for Uncertainty-Aware
Dynamic Navigation
Zhanteng Xie, Student Member, IEEE, and Philip Dames, Member, IEEE
Abstract—This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable
mobile robots to predict the future states of complex dynamic
environments. They do this by accounting for the motion of the
robot itself, the motion of dynamic objects, and the geometry of
static objects in the scene, and they generate a range of possible
future states of the environment. These prediction algorithms
are software-optimized for real-time performance for navigation
in crowded dynamic scenes, achieving 10 times faster inference
speed and 3 times less memory usage than the original engines.
Three simulated and real-world datasets collected by different
robot models are used to demonstrate that these proposed
prediction algorithms are able to achieve more accurate and
robust stochastic prediction performance than other algorithms.
Furthermore, a series of simulation and hardware navigation
experiments demonstrate that the proposed predictive uncertaintyaware navigation framework with these stochastic prediction
engines is able to improve the safe navigation performance of
current state-of-the-art model- and learning-based control policies.
Index Terms—Deep Learning in Robotics and Automation,
Reactive and Sensor-Based Planning, Learning and Adaptive
Systems, Environment Prediction.
I. INTRODUCTION
A
UTONOMOUS mobile robots are beginning to enter
people’s lives and try to help us provide different lastmile delivery services, such as moving goods in warehouses
or hospitals and assisting grocery shoppers [1]–[3]. To realize
this vision, mobile robots are required to safely and efficiently
navigate through complex and dynamic environments filled
not only with static obstacles (e.g., tables, chairs, and walls)
but also with many moving people and/or other mobile robots.
The first prerequisite for robots to navigate and perform tasks
is to use their sensors to perceive the surrounding environment.
This work focuses on the next step, which is to accurately and
reliably predict how the surrounding environment will change
based on these sensor data, as shown in Fig. 1. This will allow
to proactively take actions based on their predictions and their
prediction uncertainties to avoid potential future collisions,
a key part of improving autonomous robot navigation. Note
that since this general perception-prediction-control navigation
framework is a complex system, it is very important to make
*This work was funded by Temple University.
Zhanteng Xie and Philip Dames are with the Department of Mechanical
Engineering, Temple University, Philadelphia, PA, USA {zhanteng.xie,
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Multimedia are available: https://youtu.be/xJBtWQDLU04
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Occupancy Grid Map: Prediction
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OGM
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Fig. 1. A simple illustration of the occupancy grid map prediction problem. In
a complex dynamic environment with many pedestrians, robots, tables, chairs
and walls, colored arrows indicate the velocity of each agent.
these algorithms hardware-friendly, especially for mobile robots
with limited resources.
In this article, we propose a family of deep neural networkbased Stochastic Cartographic Occupancy Prediction Engines
(i.e., SCOPE, SCOPE++, and SO-SCOPE) for resourceconstrained mobile robots to provide stochastic future state
predictions and enable uncertainty-aware navigation in crowded
dynamic scenes. This article is an evolution of our previous
conference work [4], in which we defined the architecture of
the SCOPE and SCOPE++ predictors,1 validated their ability
to predict occupancy grid maps (OGMs), and demonstrated
their utility for robotic navigation. The primary extensions of
[4] are a more in-depth analysis of SCOPE and SCOPE++,
software optimization of the performance of those predictors
for real-time usage onboard mobile robots (i.e., SO-SCOPE),
and additional experimentation to demonstrate the performance
of SO-SCOPE for prediction and navigation. Therefore, integrating our previous work [4] and providing more technical
details and more complete experiments, this article presents
six primary contributions:
1
In our previous work [4] we used the acronym SOGMP (Stochastic
Occupancy Grid Map Predictor) instead of SCOPE.
arXiv:2407.00144v1 [cs.RO] 28 Jun 2024
2
1) We design an algorithmic pipeline called SCOPE++ that
can use a short history of robot odometry and lidar
measurements to predict a distribution of potential future
robot/environment states. SCOPE++ includes modules
to compensate for ego-motion of the robot, to segment
static/dynamic objects in the scene, to predict future scenes
using a ConvLSTM network, and to sample other future
scenes using a variational autoencoder (VAE).
2) We analyze the running time and memory usage of
each module of SCOPE++ to identify computational
bottlenecks. Based on this, we compress the VAE by
performing an in-depth statistical analysis of its statistics
and by using knowledge distillation techniques. The
resulting software-optimized SCOPE (SO-SCOPE) is able
to run in real-time on mobile robot hardware.
3) We validate the ability of our SCOPE predictors (i.e.,
SCOPE++, SCOPE, and SO-SCOPE) to predict OGMs
using three OGM datasets, each of which comes from a
different robot model. We find that SCOPE has a smaller
absolute error, higher structural similarity, higher tracking
accuracy, and lower computational resource demands than
other state-of-the-art approaches (i.e., ConvLSTM [5],
DeepTracking [6], and PhyDNet [7]). We also perform
a detailed analysis of the correctness, diversity, and
consistency of the uncertainty estimates from SCOPE.
4) We propose a costmap-based prediction uncertainty-aware
navigation framework to incorporate OGM prediction
and its uncertainty information into current existing
navigation control policies (model-based or learningbased) to improve their safe navigation performance in
crowded dynamic scenes.
5) We validate the navigation performance in simulated 3D
environments with varying crowd densities and in realworld experiments. We find that the predictive uncertaintyaware navigation framework combined with our proposed
SCOPE family can improve the navigation performance
and safety of extant control policies relative to state-ofthe-art solutions, including a model-based controller [8],
a supervised learning-based approach [9], and two deep
reinforcement learning (DRL)-based approaches [10], [11].
6) We open source the OGM prediction code with the
OGM dataset (https://github.com/TempleRAIL/scope) and
its predictive uncertainty-aware navigation framework
(https://github.com/TempleRAIL/scope nav).