Field Robotics Center Seminar
Carnegie Mellon University
Belief Space Planning for Reducing Terrain Relative Localization Uncertainty in Noisy Elevation Maps
Abstract Accurate global localization is essential for planetary rovers to reach science goals and mitigate mission risk. Planetary robots cannot currently use GPS or infrastructure for navigating, and hence rely on terrain for determining global position. Terrain relative navigation (TRN) compares planetary rover-perspective images and 3D models to existing satellite orbital imagery and digital elevation [...]
From Robust Real-time SLAM to Safe Collision Avoidance
Abstract State estimation plays a critical role in a robotic system. The problem is to know where the robot is and how it is oriented. This is very often a building block in the navigation system, which modules in charge of higher level tasks are relied on. Challenges are to carry out state estimation in [...]
Composable Benchmarks for Safe Motion Planning on Roads
Abstract Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are [...]
Learning Deep Multimodal Features for Reliable and Comprehensive Scene Understanding
Abstract Robust scene understanding is a critical and essential task for autonomous navigation. This problem is heavily influenced by changing environmental conditions that take place throughout the day and across seasons. In order to learn models that are impervious to these factors, mechanisms that intelligently fuse features from complementary modalities and spectra have to be [...]
Carnegie Mellon University
Learning Reactive Flight Control Policies: from LIDAR measurements to Actions
Abstract The end goal of a reactive flight control pipeline is to output control commands based on local sensor inputs. Classical state estimation and control algorithms break down this problem by first estimating the robot’s velocity and then computing a roll and pitch command based on that velocity. However, this approach is not robust in [...]
Carnegie Mellon University
Autonomous 3D Reconstruction in Underwater Unstructured Scenes
Abstract Reconstruction of marine structures such as pilings underneath piers presents a plethora of interesting challenges. It is one of those tasks better suited to a robot due to harsh underwater environments. Underwater reconstruction typically involves human operators remotely controlling the robot to predetermined way-points based on some prior knowledge of the location and model [...]
Carnegie Mellon University
Wire Detection, Reconstruction, and Avoidance for Unmanned Aerial Vehicles
Abstract Thin objects, such as wires and power lines are one of the most challenging obstacles to detect and avoid for UAVs, and are a cause of numerous accidents each year. This thesis makes contributions in three areas of this domain: wire segmentation, reconstruction, and avoidance. Pixelwise wire detection can be framed as a binary [...]
Carnegie Mellon University
Toward Invariant Visual Inertial State Estimation using Information Sparsification
Abstract In this work, we address two current challenges in real-time visual-inertial odometry (VIO) systems - efficiency and accuracy. To this end, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification. To bound computational complexity, fixed-lag smoothers perform marginalization of variables but consequently deteriorate accuracy and [...]
Carnegie Mellon University
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Abstract: Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for obstacles, which result in jerky movement and [...]
Visual SLAM with Semantic Scene understanding
Abstract: Simultaneous localization and mapping (SLAM) has been widely used in autonomous robots and virtual reality. It estimates the sensor motion and maps the environment at the same time. The classic sparse feature point map of visual SLAM is limited for many advanced tasks including robot navigation and interactions, which usually require a high-level understanding of [...]