ASL Publications

Publications I have contributed to while working as a Senior Reseacher at the Autonmous Systems Lab

JFR 2018:  A framework for maximum likelihood parameter identification applied on MAVs  (3rd Author)

With the growing availability of agile and powerful micro aerial vehicles (MAVs), accurate modeling is becoming more important. Especially for highly dynamic flights, model-based estimation and control combined with a good simulation framework is key. While detailed models are available in the literature, measuring the model parameters can be a time-consuming task and requires access to special equipment or facilities. In this paper, we propose a principled approach to accurately estimate physical parameters based on a maximum likelihood (ML) estimation scheme. Unlike many current methods, we make direct use of both raw inertial measurement unit measurements and the rotor speeds of the MAV. We also estimate the spatial-temporal alignment to a modular pose sensor. The proposed ML-based approach finds the parameters that best explain the sensor readings and also provides an estimate of their uncertainty. Although we derive the proposed method for use with an MAV, the approach is kept general and can be extended to other sensors or flying platforms. Extensive evaluation on simulated data and on real-world experimental data demonstrates that the approach yields accurate estimates and exhibits a large region of convergence. Furthermore, we show that the estimation can be performed using only on-board sensing, requiring no external infrastructure.

Submitted to IROS 2018:  Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning  (2nd Author)

Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space. However, creating compact and sparse map representations that can be efficiently used for planning for such robots is still an open problem. In this paper, we take maps built from noisy sensor data and construct a sparse graph containing topological information that can be used for 3D planning. We use a Euclidean Signed Distance Field, extract a 3D Generalized Voronoi Diagram (GVD), and obtain a thin skeleton diagram representing the topological structure of the environment. We then convert this skeleton diagram into a sparse graph, which we show is resistant to noise and changes in resolution. We demonstrate global planning over this graph, and the orders of magnitude speed-up it offers over other common planning methods. We validate our planning algorithm in real maps built onboard an MAV, using RGB-D sensing.

Submitted to IROS 2018:  A Scalable and Consistent TSDF-based Dense Mapping Approach  (2nd Author)

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. 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.

Submitted to RAM:  Voliro: An Omnidirectional Hexacopter With Tiltable Rotors  (6th Author)

Extending the maneuverability of unmanned areal vehicles promises to yield a considerable increase in the areas in which these systems can be used. Some such applications are the performance of more complicated inspection tasks and the generation of complex uninterrupted movements of an attached camera. In this paper we address this challenge by presenting Voliro, a novel aerial platform that combines the advantages of existing multi-rotor systems with the agility of omnidirectionally controllable platforms. We propose the use of a hexacopter with tiltable rotors allowing the system to decouple the control of position and orientation. The contributions of this work involve the mechanical design as well as a controller with the corresponding allocation scheme. This work also discusses the design challenges involved when turning the concept of a hexacopter with tiltable rotors into an actual prototype. The agility of the system is demonstrated and evaluated in realworld experiments. 

ICRA 2018:  Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles  (2nd Author)

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner.
We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.

IROS 2017:  Voxblox: Building 3d signed distance fields for planning  (2nd Author)

Truncated Signed Distance Fields (TSDFs) have become a popular tool in 3D reconstruction, as they allow building very high-resolution models of the environment in realtime on GPU. However, they have rarely been used for planning on robotic platforms, mostly due to high computational and memory requirements. We propose to reduce these requirements by using large voxel sizes, and extend the standard TSDF representation to be faster and better model the environment at these scales.
We also propose a method to build Euclidean Signed Distance Fields (ESDFs), which are a common representation for planning, incrementally out of our TSDF representation. ESDFs provide Euclidean distance to the nearest obstacle at any point in the map, and also provide collision gradient information for use with optimization-based planners. We validate the reconstruction accuracy and real-time performance of our combined system on both new and standard datasets from stereo and RGB-D imagery. The complete system will be made available as an open-source library called voxblox.

IROS 2016:  Generalized information filtering for MAV parameter estimation  (5th Author)

In this paper we present a new estimation algorithm that allows for the combination of information from any number of process and measurement models. This adds more flexibility to the design of the estimator and in our case avoids the need for state augmentation. We achieve this by adapting the maximum likelihood formulation of the Kalman Filter, and thereby represent all measurement models as residuals. Posing the problem in this form allows for the straightforward integration of any number of (nonlinear) constraints between two subsequent states. To solve the optimization we present a closed form recursive set of equations that directly marginalizes out information that is not required, this leads to an efficient and generic implementation. The new algorithm is applied to parameter estimation on MAVs which have two dynamic models, the MAV dynamic model and the IMU-driven model. We show the benefits and limitations of the new filtering approach on a simplified simulation example and on a real MAV system.

IROS 2016:  Continuous-time trajectory optimization for online UAV replanning  (3rd Author)

Multirotor unmanned aerial vehicles (UAVs) are rapidly gaining popularity for many applications. However, safe operation in partially unknown, unstructured environments remains an open question. In this paper, we present a continuous-time trajectory optimization method for real-time collision avoidance on multirotor UAVs. We then propose a system where this motion planning method is used as a local replanner, that runs at a high rate to continuously recompute safe trajectories as the robot gains information about its environment. We validate our approach by comparing against existing methods and demonstrate the complete system avoiding obstacles on a multirotor UAV platform.

SSR 2016:  Collaborative Localization of Aerial and Ground Robots through
Elevation Maps
 (4th Author)

Collaboration between aerial and ground robots can benefit from exploiting the complementary capabilities of each system, thereby improving situational awareness and environment interaction. For this purpose, we present a localization method that allows the ground robot to determine and track its position within a map acquired by a flying robot. To maintain invariance with respect to differing sensor choices and viewpoints, the method utilizes elevation maps built independently by each robot’s onboard sensors. The elevation maps are then used for global localization: specifically, we find the relative position and orientation of the ground robot using the aerial map as a reference. Our work compares four different similarity measures for computing the congruence of elevation maps (akin to dense, image-based template matching) and evaluates their merit. Furthermore, a particle filter is implemented for each similarity measure to track multiple location hypotheses and to use the robot motion to converge to a unique solution. This allows the ground robot to make use of the extended coverage of the map from the flying robot. The presented method is demonstrated through the collaboration of a quadrotor equipped with a downward-facing monocular camera and a walking robot equipped with a rotating laser range scanner

RSS Workshop 2016:  Signed distance fields: A natural representation for both mapping and planning (3rd Author)

How to represent a map of the environment is a key question of robotics. In this paper, we focus on suggesting a representation well-suited for online map building from vision-based data and online planning in 3D. We propose to combine a commonly-used representation in computer graphics and surface reconstruction, projective Truncated Signed Distance Field (TSDF), with a representation frequently used for collision checking and collision costs in planning, Euclidean Signed Distance Field (ESDF), and validate this combined approach in simulation. We argue that this type of map is better-suited for robotic applications than existing representations.

Also see my
Work From The ACFR