Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments

Loop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM

Explaining how to play real-time strategy games

Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior

Chain: A Dynamic Double Auction Framework for Matching Patient Agents

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile