Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach

In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps from visual sensors providing depth information. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a collection of overlapping TSDF subvolumes. These subvolumes are localized through feature-based camera tracking and bundle adjustment. Our main contribution is a pipeline for identifying stable regions in the map, and to fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on a publicly available dataset and simulation engine, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps. Finally we demonstrate our approach running in real-time on-board a lightweight MAV.Comment: 8 pages, 5 figures, conferenc

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

The autonomous target search problem for Unmanned Aerial Vehicles (UAV) in urban environments requires solving a 3D path planning problem for maximal information gain, given a restricted flight duration. In this paper, we propose a general, Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for the target search problem which uses active perception. The main contribution is the layered optimization approach that balances the exploration-exploitation trade-off through a Bayesian Optimization (BO) framework and simultaneously optimizes the 3D path using a standard optimizer. The planner simultaneously trades off between information gain, field coverage, altitude-dependent sensor performance, collision avoidance, target re-observation and Field of View (FoV) while planning. Through experiments in a simulated environment, we show that the proposed approach outperforms a pure exploratory IPP planner, a coverage planner, and a random sampling planner by demonstrating the fastest decrease in error related to target position estimates. Furthermore, we demonstrate the planner in simulations of varying complexity and obstacle density, demonstrating its applicability to a range of environments. Finally, we combine the proposed planning approach with an existing human detection pipeline, and demonstrate its efficacy in locating human victims in a realistic simulated environment

Range-Inertial Estimation for Airborne Wind Energy

An estimation approach is presented for an autonomous tethered kite system for the purpose of airborne wind energy generation. Accurate estimation of the kite state is critical to the performance of automatic flight controllers. We propose an estimation scheme which fuses measurements from range sensing, based on ultra-wideband radios, and inertial readings from an inertial measurement unit. Ranges are measured between a transceiver fixed to the moving kite body and a number of static range beacons scattered on the ground. Estimates are computed using the multiplicative extended Kalman filtering scheme with a sensor-driven kinematic process model using a quaternion representation of the kite attitude. Furthermore, we assume only approximate prior knowledge of the range beacon locations and consider the problem of estimating the kite state and localizing the range beacons simultaneously. We present results of the estimator tested within a simulation environment of an airborne wind energy system and compare performance to an existing estimation scheme based on tether-angles and tether-length measurements

Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are expected to play an increasing role, can often be approximated as planar. Place recognition in this context remains challenging: 2D lidar scans contain scant information with which to characterize, and therefore recognize, a location. This paper introduces a novel approach aimed at addressing this problem. At its core, the system relies on the use of the distance function for representation of geometry. This representation allows extraction of features which describe the geometry of both surfaces and free-space in the environment. We propose a feature for this purpose. Through evaluations on public datasets, we demonstrate the utility of free-space in the description of places, and show an increase in localization performance over a state-of-the-art descriptor extracted from surface geometry

Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps

Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed distance function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph, is available as open source.ISSN:2377-376