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

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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
   

2.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).