RFID Technology-based Mapping and Exploration by Humans and
Mobile Robots
Dr. Alexander KleinerInstitut für Informatik
Arbeitsgruppe “Grundlagen der Künstlichen Intelligenz”, Prof. Dr. Bernhard Nebel
Albert-Ludwigs-Universität Freiburg
[email protected]/~kleiner
A. Kleiner RFID Technology-based Mapping and Exploration by Humans and Mobile Robots 2
Related robotics activities of our groupCompetitions we recently participated
Rescue robots (RoboCup Rescue)
Multi-robot exploration (RoboCup Rescue Sim.)
RFID-based SLAM
This talk
Fast robots (Sick Robot Day)
All-terrain navigating robots (TechX challenge)
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MotivationThe “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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IntroductionMapping 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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IntroductionSolutions 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-SLAMHardware Setting Robot & Human
Robot:- 4WD (four shaft encoders)- Inertial Measurement Unit (IMU)- RFID antenna for detecting tags lying beneath the robot- RFID deploy device
Human:-IMU for step counting and angle estimation- RFID glove
Glove for sensing RFIDs (TZI Bremen)
13.56 MHz RFIDs
RFID antenna and deploy device
IMUZerg robot
RFID:- 13.56 MHz (short range below a meter)- 2048 Bit RAM, programmable by the user
Test person
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RFID-SLAMAutonomous deployment of RFIDs
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Robot pose trackingKalman 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 trackingSlip 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
Odometry distance estimate with 3σ bound compared to ground truth computed from laser-
based scan matching
Conventional odometry: covariance bound does
not hold
Slippage-sensitive
odometry: Reduced
distance error within valid
bounds
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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
• Kalman filter-based pose tracking from increments yields estimate d = (x,y,θ) with 3x3 covariance matrix Σ
Acceleration patterns during walking
Tracked pose (green)
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• Analogy to spring-mass: Find low energy arrangement of springs (estimates ) and connected masses (nodes):
• Estimation of inter-node displacements by pose tracking methods• Construction of joint graph G from all observations , consisting of measured
distances and 3x3 covariance matrix • Loops are detected if nodes have been observed twice
– Modeled by an observation edge with , where Δθ denotes the angle difference, and covariance reflecting max. detection range of the antenna
• From G a globally consistent map is calculated by minimization of the Mahalanobis distance (Lu & Milios 97) :
Centralized RFID-SLAMBuilding globally consistent maps
ijij Σ,d̂
3
1
1
2
,0,0d̂ ii
ijΣijij Σ,d̂
ijijij yx ̂,ˆ,ˆd̂ ij
iiΣ
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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 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
ijij Σ,d̂
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Decentralized optimization of A2
Second agent trajectory
Graph A2
12
3
4
5
DRFID-SLAM cont.Example
RFID node
ijij Σ,d̂
Decentralized optimization of A3
First agent trajectory
Graph A1
12
3
Third agent trajectory
Graph A3
1 2
3
4
5
6
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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 robotsCovariance bounds
3σ bounds from slippage-sensitive odometry
3σ bounds after the optimization
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RFID-SLAM by a team of humansCentralized graph optimization in a large-scale environment
Pedestrian tracks recorded in the City of Freiburg
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 ExperimentDecentralized 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 workSLAM with active RFID
Robot team with 9 sensor nodes
Zigbee Sensor node developed in Freiburg (Dept. of Microsystems
Engineering)
Sensor nodes placed outdoors for experiments
USARSim environment for simulated large scale experiments
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SLAM with active RFIDDistance 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 RFIDBearing 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 Step-wise integration yields location estimate
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SLAM with active RFIDResults from USARSim experiments
Results from experiments on different USARSim maps
Odometry on “Pywood” map
RSLAM on “Plywood” map Groundtruth of “Plywood” map
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SLAM with active RFIDResults from real world experiments
Robot odometry RSLAM
Experiment 1:
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 explorationHybrid: 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 explorationNavigation 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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Discretized visited areas counted in memory
π
Local exploration cont.Discretization of the node’s vicinity π
RFID node
Robot trajectories
Relative addressing!
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Results Local Team CoordinationVirtual rescue scenarios from NIST (RoboCup’06)
Each color denotes the path of a single robot
Largest explored area (by 8 robots)
Final 1 (indoor, 1276m2) Final 2 (outdoor. 1203m2)
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Global explorationTask 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)
Robot start nodes
Goal nodes
Multi-robot plan
Conflicts vs. # of robots: Greedy (red), GA assignment (blue), GA sequence (green)
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Rescue Virtual CompetitionVideos from RoboCup’06
Semi-Final`06 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