Toward an object-based semantic memory for long-term operation of mobile service robots

Throughout a lifetime of operation, a mobile service robot needs to acquire, store and update its knowledge of a working environment. This includes the ability to identify and track objects in different places, as well as using this information for interaction with humans. This paper introduces a long-term updating mechanism, inspired by the modal model of human memory, to enable a mobile robot to maintain its knowledge of a changing environment. The memory model is integrated with a hybrid map that represents the global topology and local geometry of the environment, as well as the respective 3D location of objects. We aim to enable the robot to use this knowledge to help humans by suggesting the most likely locations of specific objects in its map. An experiment using omni-directional vision demonstrates the ability to track the movements of several objects in a dynamic environment over an extended period of time

Robust Shared Objects for Non-Volatile Main Memory

Research in concurrent in-memory data structures has focused almost exclusively on models where processes are either reliable, or may fail by crashing permanently. The case where processes may recover from failures has received little attention because recovery from conventional volatile memory is impossible in the event of a system crash, during which both the state of main memory and the private states of processes are lost. Future hardware architectures are likely to include various forms of non-volatile random access memory (NVRAM), creating new opportunities to design robust main memory data structures that can recover from system crashes. In this paper we advance the theoretical foundations of such data structures in two ways. First, we review several known variations of Herlihy and Wing\u27s linearizability property that were proposed in the context of message passing systems but also apply in our NVRAM-based model, we discuss the limitations of these properties with respect to our specific goals, and we propose an alternative correctness condition called recoverable linearizability. Second, we discuss techniques for implementing shared objects that satisfy such properties with a focus on wait-free implementations. Specifically, we demonstrate how to achieve different variations of linearizability in our model by transforming two classic wait-free constructions

Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking