Author: Blake Hament Email: blakehament@gmail.com
Date: Last modified on 04/20/2017
Keywords: UGV, UAV, Localization, Navigation, Visual Servoing, Heterogeneous Robot Cooperation

Zip file containing all papers coming soon… Password Protected Papers Zip File

====1. Air-Ground Localization and Map Augmentation Using Monocular Dense Reconstruction====
Publisher: IROS 2013
Keywords (platform, field of research, algorithm or approach/methodologies, more details ): image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays

Bibtex:

@INPROCEEDINGS{6696924, author={C. Forster and M. Pizzoli and D. Scaramuzza}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Air-ground localization and map augmentation using monocular dense reconstruction}, year={2013}, pages={3971-3978}, keywords={image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays}, doi={10.1109/IROS.2013.6696924}, ISSN={2153-0858}, month={Nov},}

This paper describes: An algorithm for combining scans from a UAV and UGV

The authors present (simulations, experiments, theory): Algo for combining maps from a UGV and UAV: egomotion estimation (SLAM on UAV and UGV respectively); Dense Reconstruction (compute multiple depth maps from UAV data, use cost function to fuse); Global localization (align UGV and UAV maps with cost function and Zero Mean Sum of Squared Differences); Pose Refinement (Iterative Closest Point)

From this presentation, the paper concludes that: The algo presented is superior to existing methods because it field tests indicate much faster runtime (ms)

From the state-of-the-art, the paper identifies challenges in: Issues in past like algo’s only working for flat planes, processing time being too slow

The paper addresses these challenges by: adding filters and regularization

The results of this approach are: Faster processing times during localization and mapping

The paper presents the following theoretical principles: (1) Simultaneous UAV/UGV SLAM;(2) Dense Reconstruction; (3) Monte Carlo Global Localization; (4) Pose Refinement

The principles are explained (choose: well/fairly/poorly): fairly

For example (fill-in-the-blank e.g. the equations, graphs, figures),: Figure 6 show the (choose: correct/questionable) application of the principles: correct

But the costmap/Monte-Carlo based global alignment in Figure 8 was difficult to interpret

From the principles and results, the paper concludes: (1) the proposed method allows fusion of maps captured from very different perspectives; (2) this was demonstrated to be valuable because the UGV's map of a 3D structure was significantly enhanced by fusion with UAV map

Blake liked this paper because:

1. It provided a full overview of the authors' localization and mapping pipeline.
2. The authors gave a very clear description of their Dense Reconstruction algo that helped me better understand the principles behind it.
3. The authors employ excellent graphs to help the reader visualize trajectory errors and translation errors from point cloud operations.

I disliked this paper because: I had some trouble identifying what was being represented in some of the figures due to small image sizes and less-than-helpful captions ;
I would have liked to see Much more detail on the Monte-Carlo global localization

Three things I learned from this paper were:

1. Digital Surface Models
2. Dense Reconstruction
3. Iterative Closest Point algo
   

Time reading and annotating: ~ 1.5 hours

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2.COOPERATIVE GROUND AND AIR SURVEILLANCE
Publisher: IEEE 2006
Keywords (platform, field of research, algorithm or approach/methodologies, more details ): aerospace robotics;aircraft;cooperative systems;decentralised control;mobile robots;remotely operated vehicles;sensors;surveillance;telerobotics;air surveillance;cooperative system;decentralized control;ground surveillance;onboard sensors;unmanned aerial vehicles;unmanned ground vehicles;Cameras;Land vehicles;Object detection;Robot kinematics;Robot sensing systems;Robot vision systems;Robotics and automation;Surveillance;Uncertainty;Unmanned aerial vehicles
Bibtex:

@ARTICLE{1678135, author={B. Grocholsky and J. Keller and V. Kumar and G. Pappas}, journal={IEEE Robotics Automation Magazine}, title={Cooperative air and ground surveillance}, year={2006}, volume={13}, number={3}, pages={16-25}, keywords={aerospace robotics;aircraft;cooperative systems;decentralised control;mobile robots;remotely operated vehicles;sensors;surveillance;telerobotics;air surveillance;cooperative system;decentralized control;ground surveillance;onboard sensors;unmanned aerial vehicles;unmanned ground vehicles;Cameras;Land vehicles;Object detection;Robot kinematics;Robot sensing systems;Robot vision systems;Robotics and automation;Surveillance;Uncertainty;Unmanned aerial vehicles}, doi={10.1109/MRA.2006.1678135}, ISSN={1070-9932}, month={Sept},}

This paper describes: Active sensing with a team of heterogeneous robots for target localization and collaborative mapping

The authors present (simulations, experiments, theory):target location estimation equation based on uncertainty of measurements

From this presentation, the paper concludes that: robot teams can more efficiently search and scan an area by using the measurement uncertainties to “information surf” – picking trajectories based on following the highest information gain gradient

From the state-of-the-art, the paper identifies challenges in: real-time processing of localizing, navigation, and mapping due to high dimensionality of data

The paper addresses these challenges by: Building on decentralized estimation algorithms from linear dynamic models with assumptions of Guassian noise; active sensor network (ASN); certainty grids

The results of this approach are: the benefits of this approach include active sensing, decentralized processing, measurement trajectories that are more efficient and take advantage of the inherent strengths and weaknesses of each robot platform

The paper presents the following theoretical principles:

1. Information Gradients/Surfing
2. Active Sensor Network
3. Gaussian Noise in point cloud

The principles are explained (choose: well/fairly/poorly): fairly

For example (fill-in-the-blank e.g. the equations, graphs, figures),: Figure 8. show the (choose: correct/questionable) application of the principles: correct (great representation of the iso-mutual information contours the robot experiences during “information surfing”

From the principles and results, the paper concludes:

1. This method allows control of heterogenous robots without tailoring to the robots specific capabilities.
2. Every robot has it's own certainty grid containing computed possibilities of target detection at various position.
3. This certainty grid changes constantly as new information is introduced
4. This approach is easily scalable

Blake liked this paper because:

1. Excellent figures
2. Information surfing is a very interesting concept that I am excited to apply
3. I enjoyed their treatment of probabilities
   

I disliked this paper because: No complaints, great paper
I would have liked to see All of the work was done in 2D with height map projections to capture 3D of the environment. It would be interesting to see this work applied to a complex 3D environment like a building in which 2D points in the ground plane can map to multiple heights.

Three things I learned from this paper were:

1. Information Surfing
2. Control of heterogenous robots, independent of specific capabilities of the specific robots
3. Reactive Controllers

Time reading and annotating: ~ 1.5 hours

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3.ISSUES IN COOPERATIVE AIR/GROUND ROBOTIC SYSTEMS
Publisher: Springer Tracts in Advanced Robotics 2010
Keywords (platform, field of research, algorithm or approach/methodologies, more details ): UAV, UGV, Cooperation, Taxonomy
Bibtex:

@Inbook{Lacroix2011, author=“Lacroix, Simon and Le Besnerais, Guy”, editor=“Kaneko, Makoto and Nakamura, Yoshihiko”, title=“Issues in Cooperative Air/Ground Robotic Systems”, bookTitle=“Robotics Research: The 13th International Symposium ISRR”, year=“2011”, publisher=“Springer Berlin Heidelberg”, address=“Berlin, Heidelberg”, pages=“421–432”, isbn=“978-3-642-14743-2”, doi=“10.1007/978-3-642-14743-2_35”, url=“http://dx.doi.org/10.1007/978-3-642-14743-2_35” }

This paper describes: Cooperation and perception schemas for ground-aerial robot teams

The authors present (simulations, experiments, theory): a taxonomy of UGV-UAV search/mapping schemes

From this presentation, the paper concludes that: The authors suggest that current G-A mapping techniques can be improved by associating data according to geometric primitives present in the environment

From the state-of-the-art, the paper identifies challenges in: stitching together images or whole maps from UGV and UAV

The paper addresses these challenges by: suggesting the addition of models to help estimation like models for the motion of the robots and the geometry of targets of interest

The results of this approach are: G-A robot teams can cooperate in 3 different scenarios (either G or A is the supporter of the other robot, or they are ~equal cooperators); in perceiving, must solve the problems of data registration and fusion

The paper presents the following theoretical principles:

1. Localization
2. Spin-images
3. Traversability models
4. Gemetric models
5. Navigation supports
   

The principles are explained (choose: well/fairly/poorly): well

For example (fill-in-the-blank e.g. the equations, graphs, figures),: Fig. 5 show the (choose: correct/questionable) application of the principles: correct

From the principles and results, the paper concludes:

1. The key problem w/ UGV-UAV cooperation is data registration and fusion
2. Time constraints are salient in designing the algo's
3. Use prior knowledge like motion projections and geometric properties to improve data fusion Blake liked this paper because: - Solid overview of UGV-UAV search chemes - Good figures
   I disliked this paper because: information was not very in-depth
   I would have liked to see a taxonomy that extends beyond search/ mapping applications
   Three things I learned from this paper were:
   - Transversability Models - Navigation Support - UGV-UAV cooperation schemes
   Time reading and annotating: ~ 1 hour — 3.PLANNING FOR A GROUND-AIR ROBOTIC SYSTEM WITH COLLABORATIVE LOCALIZATION
   Publisher: IROS 2013
   Keywords (platform, field of research, algorithm or approach/methodologies, more details ): image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays
   Bibtex:
   @INPROCEEDINGS{7487146, author={J. Butzke and K. Gochev and B. Holden and E. J. Jung and M. Likhachev}, booktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)}, title={Planning for a ground-air robotic system with collaborative localization}, year={2016}, pages={284-291}, keywords={autonomous aerial vehicles;PAD planner;SLC planner;UGV-UAV team operating indoors;collaborative localization;controller-based motion primitives;ground-air robotic system;high-quality localization information;payload capacity;planning adaptive dimensionality;robust navigation capabilities;state lattice planner;unmanned aerial vehicles;unmanned ground vehicles;visual features;Collaboration;Lattices;Planning;Robot sensing systems;Trajectory}, doi={10.1109/ICRA.2016.7487146} This paper describes:
   The authors present (simulations, experiments, theory):
   From this presentation, the paper concludes that:
   From the state-of-the-art, the paper identifies challenges in:
   The paper addresses these challenges by:
   The results of this approach are:
   The paper presents the following theoretical principles: (1) (fill-in-the-blank); (2) (fill-in-the-blank);…. ; and (n) (fill-in-the-blank). The principles are explained (choose: well/fairly/poorly): fairly
   For example (fill-in-the-blank e.g. the equations, graphs, figures),: show the (choose: correct/questionable) application of the principles:
   From the principles and results, the paper concludes: (fill-in-the-blank); (2) (fill-in-the-blank);…. ; and (n) (fill-in-the-blank). Blake liked this paper because: (fill-in-the-blank with at least 3 reasons if possible).
   I disliked this paper because: (fill-in-the-blank);
   I would have liked to see (fill-in-the-blank).
   Three things I learned from this paper were:
   (1) (fill-in-the-blank);
   (2) (fill-in-the-blank);
   and (3) (fill-in-the-blank).
   Time reading and annotating: ~ 1.5 hours — 3.LOCALIZATION, MAPPING, MONOCULAR DENSE RECONSTRUCTION
   Publisher: IROS 2013
   Keywords (platform, field of research, algorithm or approach/methodologies, more details ): image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays
   Bibtex:
   @INPROCEEDINGS{6696924, author={C. Forster and M. Pizzoli and D. Scaramuzza}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Air-ground localization and map augmentation using monocular dense reconstruction}, year={2013}, pages={3971-3978}, keywords={image reconstruction;mobile robots;position measurement;robot vision;3D map registration;3D reconstruction;MAV monocular camera;Monte Carlo localization;air-ground localization;depth sensor;ground robot;iterative pose refinement;live dense reconstruction;map augmentation;micro aerial vehicle;monocular dense reconstruction;position estimation;sensors;vantage points;visual feature matching;Cameras;Robot kinematics;Simultaneous localization and mapping;Three-dimensional displays}, doi={10.1109/IROS.2013.6696924}, ISSN={2153-0858}, month={Nov},} This paper describes:
   The authors present (simulations, experiments, theory):
   From this presentation, the paper concludes that:
   From the state-of-the-art, the paper identifies challenges in:
   The paper addresses these challenges by:
   The results of this approach are:
   The paper presents the following theoretical principles: (1) (fill-in-the-blank); (2) (fill-in-the-blank);…. ; and (n) (fill-in-the-blank). The principles are explained (choose: well/fairly/poorly): fairly
   For example (fill-in-the-blank e.g. the equations, graphs, figures),: show the (choose: correct/questionable) application of the principles:
   From the principles and results, the paper concludes: (fill-in-the-blank); (2) (fill-in-the-blank);…. ; and (n) (fill-in-the-blank). Blake liked this paper because: (fill-in-the-blank with at least 3 reasons if possible).
   I disliked this paper because: (fill-in-the-blank);
   I would have liked to see (fill-in-the-blank).
   Three things I learned from this paper were:
   (1) (fill-in-the-blank);
   (2) (fill-in-the-blank);
   and (3) (fill-in-the-blank).
   Time reading and annotating: ~ 1.5 hours —