Information-based Active SLAM via topological feature graphs

Mu, Beipeng; Giamou, Matthew; Paull, Liam; Agha–mohammadi, Ali–akbar; Leonard, John J.; How, Jonathan P.

doi:10.1109/cdc.2016.7799127

Cited by 42 publications

(31 citation statements)

References 27 publications

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“…Mu et al use the features extracted from the environment to construct the geometric representation of the environment, which they called topological feature graph (TFG). 43 They use a sampling-based method to generate the exploration path with TFG. Xu et al use the time-varying tensor field to represent the environment and guide the robot to move.…”

Section: Environmental Characteristics-based Methodsmentioning

confidence: 99%

A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

Yang

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

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Section: Environmental Characteristics-based Methodsmentioning

confidence: 99%

A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

Yang

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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“…The link between active perception and the problem of SLAM, providing estimates about the robot's pose and its workspace, is strong [6], [7]. Both the motion and the path followed by a robot have a great impact on the performance of state estimation algorithms, so the aim in active perception is to fill the gap between path-planning and state estimation by considering the robot's state uncertainty during planning.…”

Section: Related Workmentioning

confidence: 99%

Perception-aware Path Planning for UAVs using Semantic Segmentation

Bartolomei

Teixeira

Chli

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this work, we present a perception-aware pathplanning pipeline for Unmanned Aerial Vehicles (UAVs) for navigation in challenging environments. The objective is to reach a given destination safely and accurately by relying on monocular camera-based state estimators, such as Keyframebased Visual-Inertial Odometry (VIO) systems. Motivated by the recent advances in semantic segmentation using deep learning, our path-planning architecture takes into consideration the semantic classes of parts of the scene that are perceptually more informative than others. This work proposes a planning strategy capable of avoiding both texture-less regions and problematic areas, such as lakes and oceans, that may cause large drift or failures in the robot's pose estimation, by using the semantic information to compute the next best action with respect to perception quality. We design a hierarchical planner, composed of an A * path-search step followed by B-Spline trajectory optimization. While the A * steers the UAV towards informative areas, the optimizer keeps the most promising landmarks in the camera's field of view. We extensively evaluate our approach in a set of photo-realistic simulations, showing a remarkable improvement with respect to the state-of-the-art in active perception.

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“…Some implementations such as Zhang et al (2006) use formulations that balance the objective of reducing the entropy in the map while minimizing the cost of navigation. Other implementations such as Mu et al (2016) plan control inputs that increase the number of features in the map. We note that in real-world scenarios, trajectories that conform to other constraints such as minimizing distance, minimizing battery usage, etc., are usually desired or necessary.…”

Section: Performance Improvements In Mobile Robotsmentioning

confidence: 99%

Adaptive fovea for scanning depth sensors

Tasneem

Adhivarahan

Wang

et al. 2020

The International Journal of Robotics Research

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Depth sensors have been used extensively for perception in robotics. Typically these sensors have a fixed angular resolution and field of view (FOV). This is in contrast to human perception, which involves foveating: scanning with the eyes’ highest angular resolution over regions of interest (ROIs). We build a scanning depth sensor that can control its angular resolution over the FOV. This opens up new directions for robotics research, because many algorithms in localization, mapping, exploration, and manipulation make implicit assumptions about the fixed resolution of a depth sensor, impacting latency, energy efficiency, and accuracy. Our algorithms increase resolution in ROIs either through deconvolutions or intelligent sample distribution across the FOV. The areas of high resolution in the sensor FOV act as artificial fovea and we adaptively vary the fovea locations to maximize a well-known information theoretic measure. We demonstrate novel applications such as adaptive time-of-flight (TOF) sensing, LiDAR zoom, gradient-based LiDAR sensing, and energy-efficient LiDAR scanning. As a proof of concept, we mount the sensor on a ground robot platform, showing how to reduce robot motion to obtain a desired scanning resolution. We also present a ROS wrapper for active simulation for our novel sensor in Gazebo. Finally, we provide extensive empirical analysis of all our algorithms, demonstrating trade-offs between time, resolution and stand-off distance.

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Information-based Active SLAM via topological feature graphs

Cited by 42 publications

References 27 publications

A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

Perception-aware Path Planning for UAVs using Semantic Segmentation

Adaptive fovea for scanning depth sensors

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