Neural implicit representation has recently achieved significant advancements, especially in the field of SLAM(Simultaneous Localization and Mapping). Previous NeRF-based SLAM methods have difficulties with object-level localization and reconstruction and struggle in dynamic and illumination-varied environments. We propose ONeK-SLAM, a robust object-level SLAM system that effectively combines feature points and neural radiance fields. ONeK-SLAM uses the joint information at the object level to improve localization accuracy and enhance reconstruction details. Moreover, our approach detects and eliminates dynamic objects based on the joint errors, while also harnessing the illumination invariance offered by feature points. Consequently, ONeK-SLAM achieves high-precision localization and detailed object-level mapping, even in dynamic and illumination-varying environments. Our evaluations, conducted on three public datasets that include both dynamic and variable lighting sequences, demonstrate that our method outperforms recent NeRF-based SLAM method in both localization and reconstruction.

In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular SLAM methods, due to insufficient utilization of semantic information and neglect of leveraging reverse loop closures, result in significant scale drift and pose drift when confronted with such scenarios. In this paper, we introduce SMORE-SLAM, a semantic monocular SLAM with scale correction and reverse loop closure module. We constrain scale drift by harnessing semantic information across a wide spatial extent. Furthermore, we detect and correct reverse loop closures using semantic point cloud to reduce pose drift. Experimental results on the KITTI odometry dataset and the Oxford RobotCar dataset demonstrate the capability of our research in scale correction and reverse loop closure detection, enabling a reduction in trajectory errors of monocular SLAM.

Studies the problem of maintaining materialized views of graph structured data. The base data consists of records containing identifiers of other records. The data could represent traditional objects (with methods, attributes and a class hierarchy), but it could also represent a lower-level data structure. We define simple views and materialized views for such graph structured data, analyzing options for representing record identity and references in the view. We develop incremental maintenance algorithms for these views.

No abstract available.

No abstract available.

Summary form only given. The goal of the Whips project (WareHousing Information Project at Stanford) is to develop algorithms and tools for the creation and maintenance of a data warehouse (J. Wiener et al., 1996). In particular, we have developed an architecture and implemented a prototype for identifying data changes at distributed heterogeneous sources, transforming them and summarizing them in accordance with warehouse specifications, and incrementally integrating them into the warehouse. In effect, the warehouse stores materialized views of the source data. The Whips architecture is designed specifically to fulfil several important and interrelated goals: sources and warehouse views can be added and removed dynamically; it is scalable by adding more internal modules; changes at the sources are detected automatically; the warehouse may be updated continuously as the sources change, without requiring down time; and the warehouse is always kept consistent with the source data by the integration algorithms. The Whips system is composed of many distinct modules that potentially reside on different machines. Each module is implemented as a CORBA object. They communicate with each other using ILU, a COBRA compliant object library developed by Xerox PARC.

A data warehouse stores integrated information from multiple distributed data sources. In effect, the warehouse stores materialized views over the source data. The problem of ensuring data consistency at the warehouse can be divided into two components: ensuring that each view reflects a consistent stare of the base data, and ensuring that multiple views are mutually consistent. In this paper we study the latter problem, that of guaranteeing multiple view consistency (MVC). We identify and define formally three layers of consistency for materialized views in a distributed environment. We present a scalable architecture for consistently handling multiple views in a data warehouse, which we have implemented in the WHIPS(WareHousing Information Project at Stanford) prototype. Finally, we develop simple, scalable, algorithms for achieving MVC at a warehouse.

A data warehouse is a repository of integrated information from distributed, autonomous, and possibly heterogeneous, sources. In effect, the warehouse stores one or more materialized views of the source data. The data is then readily available to user applications for querying and analysis. Figure 1 shows the basic architecture of a warehouse: data is collected from each source, integrated with data from other sources, and stored at the warehouse. Users then access the data directly from the warehouse.

A warehouse is a repository of integrated information drawn from remote data sources. Since a warehouse effectively implements materialized views, we must maintain the views as the data sources are updated. This view maintenance problem differs from the traditional one in that the view definition and the base data are now decoupled. We show that this decoupling can result in anomalies if traditional algorithms are applied. We introduce a new algorithm, ECA (for Eager Compensating Algorithm), that eliminates the anomalies. ECA is based on previous incremental view maintenance algorithms, but extra compensating queries are used to eliminate anomalies. We also introduce two streamlined versions of ECA for special cases of views and updates, and we present an initial performance study that compares ECA to a view recomputation algorithm in terms of messages transmitted, data transferred, and I/O costs.