Experimental Analysis of Quadcopter Drone Hover Constraints for Localization Improvements

In this work, we evaluate the use of aerial drone hover constraints in a multisensor fusion of ground robot and drone data to improve the localization performance of a drone. In particular, we build upon our prior work on cooperative localization between an aerial drone and ground robot that fuses data from LiDAR, inertial navigation, peer-to-peer ranging, altimeter, and stereo-vision and evaluate the incorporation knowledge from the autopilot regarding when the drone is hovering. This control command data is leveraged to add constraints on the velocity state. Hover constraints can be considered important dynamic model information, such as the exploitation of zero-velocity updates in pedestrian navigation. We analyze the benefits of these constraints using an incremental factor graph optimization. Experimental data collected in a motion capture faculty is used to provide performance insights and assess the benefits of hover constraints.

Analysis of the Unscented Transform for Cooperative Localization with Ranging-Only Information

Cooperative localization in multi-agent robotic systems is challenging, especially when agents rely on limited information, such as only peer-to-peer range measurements. Two key challenges arise: utilizing this limited information to improve position estimation; handling uncertainties from sensor noise, nonlinearity, and unknown correlations between agents measurements; and avoiding information reuse. This paper examines the use of the Unscented Transform (UT) for state estimation for a case in which range measurement between agents and covariance intersection (CI) is used to handle unknown correlations. Unlike Kalman Filter approaches, CI methods fuse complete state and covariance estimates. This makes formulating a CI approach with ranging-only measurements a challenge. To overcome this, UT is used to handle uncertainties and formulate a cooperative state update using range measurements and current cooperative state estimates. This introduces information reuse in the measurement update. Therefore, this work aims to evaluate the limitations and utility of this formulation when faced with various levels of state measurement uncertainty and errors.

UAV Position Estimation using a LiDAR-based 3D Object Detection Method

This paper explores the use of applying a deep learning approach for 3D object detection to compute the relative position of an Unmanned Aerial Vehicle (UAV) from an Unmanned Ground Vehicle (UGV) equipped with a LiDAR sensor in a GPS-denied environment. This was achieved by evaluating the LiDAR sensor's data through a 3D detection algorithm (PointPillars). The PointPillars algorithm incorporates a column voxel point-cloud representation and a 2D Convolutional Neural Network (CNN) to generate distinctive point-cloud features representing the object to be identified, in this case, the UAV. The current localization method utilizes point-cloud segmentation, Euclidean clustering, and predefined heuristics to obtain the relative position of the UAV. Results from the two methods were then compared to a reference truth solution.