RFID Technology-based Mapping
and Exploration by Humans and
Mobile Robots
Dr. Alexander Kleiner
Institut für Informatik
Arbeitsgruppe “Grundlagen der Künstlichen Intelligenz”, Prof. Dr.
Bernhard Nebel
Albert-Ludwigs-Universität Freiburg
kleiner@informatik.uni-freiburg.de
www.informatik.uni-freiburg.de/~kleiner
Related robotics activities of our group
Competitions we recently participated
Rescue robots (RoboCup Rescue)
RFID-based SLAM
Fast robots (Sick Robot Day)
Multi-robot exploration
(RoboCup Rescue Sim.)
This talk
All-terrain navigating robots (TechX challenge)
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Motivation
The “golden” 72 hours
Courtesy S. Tadokoro
Courtesy R. Murphy
Tom Haus (firemen at 9/11): “We need a tracking
system that tells us where we are, where we have
been, and where we have to go to”
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Outline
• Introduction
• RFID SLAM
– Centralized
– Decentralized (DRFID SLAM)
– Ongoing work: SLAM with active RFID
• Multi-robot exploration
– Local exploration
– Global exploration
• Conclusions
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Introduction
Mapping and Exploration within US&R environments
• Mapping: Computing globally consistent maps from pose
tracking and data association by one or multiple agents
• Exploration: Efficient coverage of an unknown environment
by one or multiple agents
• Requirements within harsh real-world domains (e.g. US&R):
– Real-time computation
– Decentralized with limited radio communication
– Mixed-initiative teams: Integration of robotic solutions into human
organizations (e.g. first responders)
• Limitations of existing solutions:
– Data association problem:
• Dynamic illumination conditions
• Unstructured environment in 3D
– “Loop closure” requirement
• Cannot be guaranteed during time critical missions (e.g. victim search)
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Introduction
Solutions presented in this talk
• Decentralized team coordination and Simultaneous
Localization And Mapping (SLAM) via local
information nodes, e.g. RFIDs
– World-wide unique labeling of places
• No data association problem
– Exchange of map pieces between teams of robots and
humans
• “Loop-closure” on joint maps
– Information sharing via local node memories
• Facilitates decentralized mapping & exploration with indirect
communication
– Topological node graph structure
• Efficient multi-robot task assignment and path planning
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Outline
• Related activities
• Introduction
• RFID SLAM
– Centralized
– Decentralized (DRFID SLAM)
– Ongoing work: SLAM with active RFID
• Multi-robot exploration
– Local exploration
– Global exploration
• Conclusions
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RFID-SLAM
Hardware Setting Robot & Human
13.56 MHz RFIDs
Zerg robot
RFID antenna
and deploy device
Robot:
- 4WD (four shaft encoders)
- Inertial Measurement Unit (IMU)
- RFID antenna for detecting tags lying
beneath the robot
- RFID deploy device
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Glove for sensing
RFIDs (TZI Bremen)
RFID:
- 13.56 MHz (short range
below a meter)
- 2048 Bit RAM,
programmable by the
user
IMU
Test person
Human:
-IMU for step counting
and angle estimation
- RFID glove
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RFID-SLAM
Autonomous deployment of RFIDs
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Robot pose tracking
Kalman filter for updating pose estimates
• Typically applied with constant error covariance
• However, particularly outdoors, wheel odometry errors
are situation dependent, e.g. the specific type of ground
• Solution: Adaptive odometry model
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Robot pose tracking
Slip detection from redundant wheel odometry
• Wheels on the same side turn
at different speeds under slip!
 Measuring with 4 shaft
encoders
• Decision tree model
for slip detection
– with the wheel-velocity
differences Δvleft, Δvright, Δvrear,
Δvfront, as classifier input
• Determination of error estimate
σslip by computing the RMS
error with scan matching GT
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Robot Pose Tracking
Slippage-sensitive odometry
Conventional
odometry:
covariance
bound does
not hold
Slippagesensitive
odometry:
Reduced
distance error
within valid
bounds
Odometry distance estimate with 3σ bound
compared to ground truth computed from laserbased scan matching
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Human pose tracking
“Odometry” from accelerometer, gyros, and magnetometer
• Human foot steps generate vertical
accelerations
• We use a method from Ladetto et al. that
extracts acceleration maxima for counting
foot steps
• Individual step length is automatically
calibrated from GPS readings (if available),
yielding distance estimates from steps
• Orientation changes are measured by
gyroscope and magnetometer
Acceleration patterns during walking
• Kalman filter-based pose tracking from
increments yields estimate d = (x,y,θ) with
3x3 covariance matrix Σ
Tracked pose (green)
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Centralized RFID-SLAM
Building globally consistent maps
•
•
•
Estimation of inter-node displacements by pose tracking methods
ˆ , Σ , consisting of
Construction of joint graph G from all observations d
ij
ij
measured distances dˆ ij  xˆij , yˆij , ˆij and 3x3 covariance matrix Σij
Loops are detected if nodes have been observed twice
– Modeled by an observation edge with dˆ ii  0,0,   , where Δθ denotes the


angle difference, and covariance Σ ii reflecting max. detection range of the
antenna
•
From G a globally consistent map is calculated by minimization of the
Mahalanobis distance (Lu & Milios 97) :
•
Analogy to spring-mass: Find low energy
arrangement of springs (estimates dˆ ij , Σij ) and
connected masses (nodes):
3
1
1
2
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Outline
• Related activities
• Introduction
• RFID SLAM
– Centralized
– Decentralized (DRFID SLAM)
– Ongoing work: SLAM with active RFID
• Multi-robot exploration
– Local exploration
– Global exploration
• Conclusions
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Decentralized RFID-SLAM (DRFID-SLAM)
Indirect communication via node memories
• The basic idea:
– To utilize the memory of nodes for learning the topology of the
graph
– Mobile agents are accumulating graphs Aj  G from observations
on their path
– Nodes are learning local graphs Ri  G representing the topology
of their vicinity
– Agents propagate information through the network, i.e. synchronize
nodes if they are in range
• Information update:
– When traveling from node i to node k:
• Add new estimate dˆ ij , Σij to Rk
• Update Aj from Rk by graph merging, and vice versa
• Double edges:
– Locally fused by adjacent nodes
– ID value for each fusion for preventing doubly counting
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DRFID-SLAM cont.
Example
22
2
111
Graph A
Graph A3 2
33
RFID node
dˆ ij , Σij
Graph A1
55
3
6
4
Second
agent
Third
agent
First
agent
trajectory
trajectory
trajectory
Decentralized optimization of A32
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RFID-SLAM jointly by humans and robots
Corrected map
Robot (orange) and pedestrian (red) odometry
Corrected track (green) compared to GPS
ground truth (blue)
-Robot driving at 1.58 m/s for 2.5 km
-10 RFIDs
- Optimization time below a second
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RFID-SLAM jointly by humans and robots
Covariance bounds
3σ bounds from slippage-sensitive
odometry
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3σ bounds after the optimization
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RFID-SLAM by a team of humans
Centralized graph optimization in a large-scale environment
Pedestrian tracks recorded in the
City of Freiburg
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Corrected RFID graph
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DRFID-SLAM (from data)
Decentralized graph optimization during the outdoor exp.
• Simulation of all 720
possible sequences of
6 agents exploring the
environment
• The more agents
visited the area, the
better the individual
map improvements
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DRFID-SLAM Experiment
Decentralized graph optimization during the outdoor exp.
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Outline
• Related activities
• Introduction
• RFID SLAM
– Centralized
– Decentralized (DRFID SLAM)
– Ongoing work: SLAM with active RFID
• Multi-robot exploration
– Local exploration
– Global exploration
• Conclusions
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Ongoing work
SLAM with active RFID
Zigbee Sensor node
developed in Freiburg
(Dept. of Microsystems
Engineering)
Sensor nodes placed
outdoors for
experiments
Robot team with 9 sensor nodes
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USARSim environment
for simulated large
scale experiments
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SLAM with active RFID
Distance estimation from RSSI (signal strength)
Relation between Transceiver-Receiver
(TR) separation d and signal strength P
(Seidel & Rapport 1992):
Path loss at reference
distance d0
Noise with
variance σ
However, many outliers in the data!
We use the RANSAC (Random Sample
Consensus) method to identify outliers.
Resulting model for the utilized Zigbee
modules
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SLAM with active RFID
Bearing estimation by voting grids
• Omni-directional antennas provide no bearing information!
• Bearing can be determined from intersections of range
measurements at different robot locations (by odometry)
• Each cell on the grid votes for the RFID location
Observations during navigation
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Step-wise integration yields location estimate
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SLAM with active RFID
Results from USARSim experiments
RSLAM on “Plywood” map
Groundtruth of “Plywood” map
Odometry on “Pywood” map
Results from experiments on different USARSim maps
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SLAM with active RFID
Results from real world experiments
Experiment 1:
Robot odometry
RSLAM
Experiment 2:
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Outline
• Related activities
• Introduction
• RFID SLAM
– Centralized
– Decentralized (DRFID SLAM)
– Ongoing work: SLAM with active RFID
• Multi-robot exploration
– Local exploration
– Global exploration
• Conclusions
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RFID-based exploration
Hybrid: local exploration and global planning
• Local exploration (LE):
– Indirect communication
– Scales-up with # of robots and environment size
– Inefficient exploration due to local minima
• Global task assignment and path planning
– Based on node graph abstraction of the environment
– Monitors LE and computes new agent-node
assignment If exploration overlap is high
– Requires communication
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Local exploration
Navigation and target selection
• Navigation based on (limited) robot-centric grid map generated from
laser ranges
• Exploration targets taken from grid frontier cells [Yamauchi, 1997]
• Coordination:
– Automatic node deployment w. r. t. a pre-defined density
– Discretization of node vicinity into equally sized patches
– Node memory for counting visits of each patch [Svennebring and
Koenig, 2004])
– Frontier selection by minimizing the following cost function:
lfi : frontier cell location,
LRS: set of nodes within range,
Pr: set of patches around node r,
d(.): the Euclidean distance
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Local exploration cont.
Discretization of the node’s vicinity π
RFID node
π
Robot
trajectories
Discretized visited areas
counted in memory
Relative
addressing!
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Results Local Team Coordination
Virtual rescue scenarios from NIST (RoboCup’06)
Largest explored area
(by 8 robots)
Each color
denotes the path
of a single robot
Final 1 (indoor, 1276m2)
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Final 2 (outdoor. 1203m2)
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Global exploration
Task assignment and planning
• Task assignment:
– Sequential robot planning to best targets [Burgard et al., 2005]
– Genetic algorithm (GA) for finding optimal planning sequence
• Score computed from multi-robot plan cost
• Initialized by greedy sequence
• Computation of multi-robot plan:
– A* time space planning to multiple goals [Bennewitz et al., 2001]
– Plan costs: joint plan length + conflict penalties (infinite if
deadlock)
– Heuristic: based on pre-computed shortest Dijkstra tree ignoring
conflicts
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Results Global Team Coordination
Task assignment and planning on node graph (USARSim outdoor map)
Goal nodes
Robot start nodes
Multi-robot plan
Conflicts vs. # of robots: Greedy (red), GA assignment (blue), GA sequence (green)
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Rescue Virtual Competition
Videos from RoboCup’06
Semi-Final`06
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Final`06
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Conclusions and Future Work
• Robust and efficient methods for DSLAM and exploration
–
–
–
–
–
Limited radio communication
No requirement for direct loop closure
Local information exchange
Joint human and robot exploration
Coordination scalable in terms of communication and
computation
• Future work:
– Experiments with far-range RFID technology, such as ZigBee
transponders
– Spontaneous node-to-node communication for synchronizing
position estimates and explored areas
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Thanks for your attention, any
questions?
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