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Thrust IIA: Environmental State Estimation and Mapping Dieter Fox (Lead) Nicholas Roy MURI 8 Kickoff Meeting 2007
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Task Objective: Human-Centered Maps Observation: Automatic map-building (SLAM) is solved sufficiently well Goal: Describe environments by higher-level concepts: Places (room, hallway, street, walkway, parking lot, …) Objects (tree, person, building, car, wall, …) Key challenges: Estimating concept types is mostly a discrete problem Complex features and relationships MURI 8 Kickoff Meeting 2007 University of Washington
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Existing Technology Human-centered mapping requires integration of high-dimensional, continuous features from multi-modal sensor data reasoning about spatial and temporal relationships Conditional Random Fields provide extremely flexible probabilistic framework for learning and inference MURI 8 Kickoff Meeting 2007 University of Washington
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Conditional Random Fields Discriminative, undirected graphical model Introduced for labeling sequence data to overcome weaknesses of Hidden Markov Models [Lafferty-McCallum-Pereira: ICML-01] Applied successfully to Natural language processing [McCallum-Li: CoNLL-03], [Roth-Yih: ICML-05] Computer vision [Kumar-Hebert: NIPS-04], [Quattoni-Collins-Darrel: NIPS-05] Robotics [Limketkai-Liao-Fox: IJCAI-05], [Douillard-Fox-Ramos: IROS-07] MURI 8 Kickoff Meeting 2007 University of Washington
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Conditional Random Fields Directly models conditional probability p(x|z) (instead of modeling p(z|x) and p(x), and using Bayes rule to infer p(x|z)). No independence assumption on observations needed! Hidden states x Observations z MURI 8 Kickoff Meeting 2007 University of Washington
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Online Object Recognition MURI 8 Kickoff Meeting 2007 [Douillard-Fox-Ramos: IROS-07, ISRR-07]
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From Laser Scans to CRFs MURI 8 Kickoff Meeting 2007 Object type of laser beam 1 Shape and appearance Object type of laser beam 2 Object type of laser beam 3 Object type of laser beam 4 Object type of laser beam n Shape and appearance …
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Visual Features MURI 8 Kickoff Meeting 2007 steerable pyramid 3-d RGB histogram 3-d HSV histogram
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Geometric Features MURI 8 Kickoff Meeting 2007
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Temporal Integration MURI 8 Kickoff Meeting 2007 ………… k-2k-1kk+1 Taking past and future scans into account can improve labeling accuracy. Match consecutive laser scans using ICP. Associated laser points are connected in CRF. Can perform online filtering or offline smoothing via BP.
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Example Trace: Car vs. Others MURI 8 Kickoff Meeting 2007 Trained on 90 labeled scans Inference via filtering in CRF
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7 Class Example Labeling MURI 8 Kickoff Meeting 2007
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7 Class Example Labeling MURI 8 Kickoff Meeting 2007
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7 Class Example Labeling MURI 8 Kickoff Meeting 2007
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7 Class Example Labeling MURI 8 Kickoff Meeting 2007
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7 Class Example Labeling MURI 8 Kickoff Meeting 2007
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Proposed Technical Advances Integrate recognition results into maps Improve results by leveraging web training data and high level object detectorss Add object types suited for target scenario Improve CRF training MURI 8 Kickoff Meeting 2007 University of Washington
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Situation Awareness via Wearable Sensors MicrophoneCamera Light sensors 2 GB SD card Indicator LEDs Records 4 hours of audio, images (1/sec), GPS, and sensor data (accelerometer, barometric pressure, light intensity, gyroscope, magnetometer)
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Soldier Activity Recognition Automatic generation of mission summaries Motion type (linger, walk, run, drive, …) Environment (inside, outside building) Events (conversations, marked via keyword) Technical challenges High-dimensional, continuous observations / features Different data rates: (1 Hz - 256 Hz) Getting labeled training data Different persons / environments MURI 8 Kickoff Meeting 2007
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Activity Model MURI 8 Kickoff Meeting 2007 e t-1 Environment indoor, outdoor, vehicle etet a t-1 atat c t-1 ctct Activity walk, run, stop, up/downstairs, drive, elevator, cover [Subramanya-Raj-Bilmes-Fox: UAI-06, ISRR-06] Sensor data High-dimensional feature vectors
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Data Visualization / Summarization MURI 8 Kickoff Meeting 2007 GPS traces Image sequence (currently in car) Timeline of soldier activities
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Milestones Goals: Real time wearable interface on cell phone Data sharing among soldiers and robots Real time display on remote laptop MURI 8 Kickoff Meeting 2007
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Milestones Year 1: Real time data sharing between wearable sensor platforms Integration of object recognition into mapping Year 2: Real time data sharing between soldiers, robots, and remote laptop Detection of specific soldier states / activities (moving, incapacitated,...)
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