Semantic Simultaneous Localization And Mapping

Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Recent advances in object recognition and semantic scene understanding, however, have made this information easier to extract than ever before, and the recent proliferation of robots in human environments demand access to reliable semantic-level mapping and localization algorithms to enable true autonomy. Furthermore, loop closure recognition based on low-level features is often viewpoint dependent and subject to failure in ambiguous or repetitive environments, whereas object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks. In this thesis, we present two solutions that incorporate semantic information into a full localization and mapping pipeline. In the first, we propose a solution method using only single-image bounding box object detections as the semantic measurement. As these bounding box measurements are relatively imprecise when projected back into 3D space and difficult to associate with existing mapped objects, we first present a general method to probabilistically compute data associations within an estimation framework and demonstrate its improved accuracy in the case of high-uncertainty measurements. We then extend this to the specific case of semantic bounding box measurements and demonstrate its accuracy in indoor and outdoor environments. Second, we propose a solution based on the detection of semantic keypoints. These semantic keypoints are not only more reliably positioned in space, but also allow us to estimate the full six degree-of-freedom pose of each mapped object. The usage of these semantic keypoints allows us to effectively reduce the problem of semantic mapping to that of the much more well studied problem of mapping point features, allowing for its efficient solution and robustness in practice. Finally, we present a method of robotic navigation in unexplored semantic environments that robustly plans paths through unknown and unexplored semantic environments towards a goal location. Through the use of the semantic keypoint-based semantic SLAM algorithm, we demonstrate the successful execution of navigation missions through on-the-fly generated semantic maps

Reactive Semantic Planning in Unexplored Semantic Environments Using Deep Perceptual Feedback

This paper presents a reactive planning system that enriches the topological representation of an environment with a tightly integrated semantic representation, achieved by incorporating and exploiting advances in deep perceptual learning and probabilistic semantic reasoning. Our architecture combines object detection with semantic SLAM, affording robust, reactive logical as well as geometric planning in unexplored environments. Moreover, by incorporating a human mesh estimation algorithm, our system is capable of reacting and responding in real time to semantically labeled human motions and gestures. New formal results allow tracking of suitably non-adversarial moving targets, while maintaining the same collision avoidance guarantees. We suggest the empirical utility of the proposed control architecture with a numerical study including comparisons with a state-of-the-art dynamic replanning algorithm, and physical implementation on both a wheeled and legged platform in different settings with both geometric and semantic goals. For more information: Kod*la

Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

With the unprecedented photometric precision of the Kepler Spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends and other instrumental signatures, obscure astrophysical signals. The Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods such as SYSREM and TFA. Variable stars are often of particular astrophysical interest so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian Maximum A Posteriori (MAP) approach where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set which is in turn used to establish a range of "reasonable" robust fit parameters. These robust fit parameters are then used to generate a Bayesian Prior and a Bayesian Posterior Probability Distribution Function (PDF) which when maximized finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection which commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian Prior PDFs are generated from fits to the light curve distributions themselves.Comment: 43 pages, 21 figures, Submitted for publication in PASP. Also see companion paper "Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves" by Martin C. Stumpe, et a

BpaB, a Novel Protein Encoded by the Lyme Disease Spirochete\u27s Cp32 Prophages, Binds to Erp Operator 2 DNA

Borrelia burgdorferi produces Erp outer surface proteins throughout mammalian infection, but represses their synthesis during colonization of vector ticks. A DNA region 5′ of the start of erp transcription, Operator 2, was previously shown to be essential for regulation of expression. We now report identification and characterization of a novel erp Operator 2-binding protein, which we named BpaB. erp operons are located on episomal cp32 prophages, and a single bacterium may contain as many as 10 different cp32s. Each cp32 family member encodes a unique BpaB protein, yet the three tested cp32-encoded BpaB alleles all bound to the same DNA sequence. A 20-bp region of erp Operator 2 was determined to be essential for BpaB binding, and initial protein binding to that site was required for binding of additional BpaB molecules. A 36-residue region near the BpaB carboxy terminus was found to be essential for high-affinity DNA-binding. BpaB competed for binding to erp Operator 2 with a second B. burgdorferi DNA-binding protein, EbfC. Thus, cellular levels of free BpaB and EbfC could potentially control erp transcription levels

\u3cem\u3eBorrelia burgdorferi\u3c/em\u3e EbfC Defines a Newly-Identified, Widespread Family of Bacterial DNA-Binding Proteins

The Lyme disease spirochete, Borrelia burgdorferi, encodes a novel type of DNA-binding protein named EbfC. Orthologs of EbfC are encoded by a wide range of bacterial species, so characterization of the borrelial protein has implications that span the eubacterial kingdom. The present work defines the DNA sequence required for high-affinity binding by EbfC to be the 4 bp broken palindrome GTnAC, where ‘n’ can be any nucleotide. Two high-affinity EbfC-binding sites are located immediately 5′ of B. burgdorferi erp transcriptional promoters, and binding of EbfC was found to alter the conformation of erp promoter DNA. Consensus EbfC-binding sites are abundantly distributed throughout the B. burgdorferi genome, occurring approximately once every 1 kb. These and other features of EbfC suggest that this small protein and its orthologs may represent a distinctive type of bacterial nucleoid-associated protein. EbfC was shown to bind DNA as a homodimer, and site-directed mutagenesis studies indicated that EbfC and its orthologs appear to bind DNA via a novel α-helical ‘tweezer’-like structure

BpaB, a novel protein encoded by the Lyme disease spirochete’s cp32 prophages, binds to erp Operator 2 DNA

Borrelia burgdorferi produces Erp outer surface proteins throughout mammalian infection, but represses their synthesis during colonization of vector ticks. A DNA region 5′ of the start of erp transcription, Operator 2, was previously shown to be essential for regulation of expression. We now report identification and characterization of a novel erp Operator 2-binding protein, which we named BpaB. erp operons are located on episomal cp32 prophages, and a single bacterium may contain as many as 10 different cp32s. Each cp32 family member encodes a unique BpaB protein, yet the three tested cp32-encoded BpaB alleles all bound to the same DNA sequence. A 20-bp region of erp Operator 2 was determined to be essential for BpaB binding, and initial protein binding to that site was required for binding of additional BpaB molecules. A 36-residue region near the BpaB carboxy terminus was found to be essential for high-affinity DNA-binding. BpaB competed for binding to erp Operator 2 with a second B. burgdorferi DNA-binding protein, EbfC. Thus, cellular levels of free BpaB and EbfC could potentially control erp transcription levels