Long-Distance Loop Closure Using General Object Landmarks

Long-Distance Loop Closure Using General Object Landmarks

Andrew Holliday and Gregory Dudek Andrew Holliday and Gregory Dudek are with the Center for Intelligent Machines, McGill University, 845 Sherbrooke St, Montreal, Canada ahollid@cim.mcgill.ca, dudek@cim.mcgill.ca

Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances. Existing approaches, however, are robust only up to a 3x difference in scale between map and query images.

We propose a novel combination of deep-learning-based object features and hand-engineered point-features that yields improved robustness to scale change, perspective change, and image noise. We conduct experiments in simulation and in real-world outdoor scenes exhibiting up to a 7x change in scale, and compare our approach against localization using state-of-the-art SIFT features. This technique is training-free and class-agnostic, and in principle can be deployed in any environment out-of-the-box.

I Introduction

In this work, we attempt to address the problem of performing metric localization in a known environment under extreme changes in visual scale. Our localization approach is based on the identification of objects in the environment, and their use as landmarks. By “objects” we here mean physical entities which are distinct from their surroundings and have some consistent physical properties of structure and appearance.

Many robotic applications involve repeated traversals of a known environment over time. In such applications, it is usually beneficial to first construct a map of the environment, which can then be used by a robot to navigate the environment in subsequent missions. Surveying the environment from a very high altitude allows complete geographic coverage of the environment to be obtained by shorter, and thus more efficient, paths through the environment by the surveyor. At the same time, a robot that makes use of this high-altitude map to localize may have mission parameters requiring it to operate at a much lower altitude.

Fig. 1: The results of SIFT features alone, and of the overall-best configuration of our system, on the largest-scale-change image pair in the dataset. Despite the scale factor of 6, the highly-distinctive object in the foreground allows our system to determine an accurate homography between the images, while SIFT features fail to do so.

One such scenario is that of performing visual surveys of benthic environments, such as coral reefs, as in  [1]. A fast-moving surface vehicle may be used to rapidly map a large area of a reef. This map may then be used by a slower-moving, but more maneuverable, autonomous underwater vehichle (AUV) such as the Aqua robot  [2], to navigate the reef while capturing imagery very close to the sea floor. Another relevant scenario is that of a robot performing loop closure over long distances while performing simultaneous localization and mapping (SLAM) in terrestrial environments. Loop closure, the recognition of a previously-viewed location when viewing it a second time, is key to accurate SLAM, and the overall accuracy of SLAM could be considerably improved if loop closure could be conducted across major changes in scale and perspective.

In scenarios such as the above, a robot must deal with the considerable change in visual scale between two perspectives, which in our scenarios may be 5 times or even greater. In some scenarios, such as in benthic environments, other factors may also intrude, such as colour-shifting due to the optical properties of water, and image noise due to particulate suspended in the water. Identifying scenes across such large changes in scale is very challenging for modern visual localization techniques. Even the most scale-robust techniques, such as Scale-Invariant Feature Transforms (SIFT), can only localize reliably under scale factors less than 3.

We consider that the hierarchical features computed by the intermediate layers of a convolutional neural network (CNN)  [3] may prove robust to changes in scale, due to their high degree of abstraction. We propose a technique for performing metric localization across significant changes in scale by identifying and describing non-semantic objects in a way that allows them to be associated between scenes. We show that these associations can be used to localize correctly under visual scale factors of 3 and greater. The proposed system does not require any environment-specific training, and in principle can be deployed out-of-the-box in arbitrary environments. The objects used by our system are defined functionally, in terms of their utility as scale-invariant landmarks, and are not limited to semantically-meaningful object categories. We demonstrate the approach both in simulation and on a novel dataset of real-world image pairs of urban scenes.

Ii Related Work

Global Localization refers to the problem of determining a robot’s location in a pre-existing map with no prior on the robot’s position  [4]. This is closely related to the problem of loop closure in SLAM, and there is a large body of literature exploring this problem. Prominent early work includes Leonard et al.  [5] and Mackenzie et al.  [6], and  [7].

Many traditional visual approaches to these problems have been based on the recognition of whole-image descriptors of particular scenes, such as GIST features  [8]. Successful instances include SeqSLAM  [9], which uses a heavily downsampled version of the input image as a descriptor, and FAB-MAP  [10], ORB-SLAM  [11], and Ho et al.  [12] which discretize point-feature descriptors to build bag-of-words histograms of the input images, starting from SURF  [13], ORB  [14], and SIFT  [15] features, respectively. Other successful results include LSD-SLAM of Engel et al.  [16] as well as Hansen et al.  [17], Cadena et al.,  [18], Liu et al.  [19] and Naseer et al.  [20]. Because whole-image descriptors encode the geometric relationships of features in the 2D image plane, images of the same world scene from different perspectives can have very different descriptors, in general no more similar than those of images of completely different places. Most such methods are thus very sensitive to changes in perspective, and depend on viewing the world from the same perspective in the mapping and localization stages.

Other global localization approaches attempt to recognize particular landmarks in an image, and use those to produce a metric estimate of the robot’s pose. SLAM++ of Salas-Moreno et al.  [21] performs SLAM by recognizing landmarks from a database of 3D object models. Linegar et al.  [22] and Li et al.  [23] both train a bank support vector machines (SVMs) to detect specific landmarks in a known environment, one SVM per landmark. More recently,  [24] made use of a Deformable Parts Model (DPM)  [25] to detect objects for use as loop-closure landmarks in their SLAM system. All of these approaches require a pre-existing database of either object types or specific objects to operate. These databases can be costly to construct, and these systems will fail in environments in which not enough landmarks belonging to the database are present.

Some work has explored the use of CNNs for global localization. PoseNet  [26] is a CNN that learns mapping from an image in an environment to the metric pose of the camera, but can only operate on the environment on which the network was trained. In Sünderhauf et al.  [27], the intermediate activations of a CNN trained for image classification were used as whole-image descriptors for place recognition, a non-metric form of global localization. Their subsequent work  [28] refined this approach by using the same descriptor technique on object proposals within an image instead of the whole image. These pre-trained deep descriptors were robust to changes in appearance and perspective. Schmidt et al.  [29] and Simo-Serra et al.  [30] have both explored the idea of learning point-feature descriptors with a CNN, which could replace classical point features in a bag-of-words model.

When exploring robustness to perspective change, all of these works only consider positional variations of at most a few meters, when the scenes exhibit within-image scale variations of tens or hundreds of meters, and when the reference or training datasets consisted of images taken over traversals of environments ranging from hundreds to thousands of meters. As a result, little significant change in scale exists between map images and query images in these experiments. To the best of our knowledge, ours is the first to attempt to combine deep object-like features and point features into a single, unified representation of landmarks. This synthesis provides superior metric localization to either technique in isolation, particularly under significant (3x and greater) changes in scale.

Iii Proposed System

The first stage of our metric localization pipeline consists in detecting objects in a pair of images, computing convolutional descriptors for them, and matching these descrpitors between images. Our approach here closely follows that used for image-retrieval by Sünderhauf et al.  [27]; we differ in using Selective Search (SS), as proposed by Uijlings et al.  [31], to propose object regions, and in our use of a more recent CNN architecture.

To extract objects from an image, Selective Search object proposals are first extracted from the image, and filtered to remove objects with bounding boxes less than 200 pixels in size and with aspect ratio greater than 3 or less than 1/3. The image regions defined by each surviving SS bounding box are then extracted from the image, rescaled to a fixed size via bilinear interpolation, and run through a CNN. We use a ResNet-50 architecture trained on the ImageNet image-classification dataset, as described in He et al.  [32]. Experiments were run using six different layers of the network as feature descriptors, and with inputs to the network of four different resolutions. The network layers and resolutions are listed in Table  I.

Having extracted objects and their descriptors from a pair of images, we perform brute-force matching of the objects between the images. Following  [28], we take the match of each object descriptor in image to be the descriptor in image that has the smallest cosine distance from , as defined in  1. Matches are validated by cross-checking; a match is only considered valid if is the most similar object to in image and is the most similar object to in image .

Input res. pool1 res2c res3d res4f res5c pool5
201k 803k 401k 200k 100k 2k
66k 262k 131k 65k 131k 8k
16k 66k 32k 66k 131k 8k
4k 16k 32k 66k 131k 8k
TABLE I: The sizes, as a number of floating-point values, of the output layers of ResNet-50 at different input resolutions. Values in bold indicate layer-resolution pairs which provided the best results in any of our experiments.

Once object matches are found, we extract SIFT features from both images, using 3 octave layers, an initial Gaussian with , an edge threshold of 10, and a contrast threshold of 0.04. For each pair of matched objects, we match SIFT features that lie inside the corresponding bounding boxes to one another. SIFT features are matched via their Euclidean distance, and cross-checking is again used to filter out bad matches. By limiting the space over which we search for SIFT matches to matched object regions, we greatly increase the accuracy of SIFT matching, and thus of the resulting metric pose estimates. As a baseline against which to compare our results, experiments were also run using SIFT alone, with no object proposals. In these baseline experiments, SIFT matching was performed in the same way, but the search for matches was conducted over all SIFT features in both images.

These point matches are used to produce a metric pose estimate. Depending on the experiment, we compute either a homography or an essential matrix , or both. In either case, the calculation of or from point correspondences is done via a RANSAC algorithm with an inlier threshold of 6, measured in pixel units.

Fig. 2: A simplified illustration of our object-detection architecture.

Iv Experiments in Simulation

Iv-a Experimental Setup

A range of experiments were conducted in simulation that imitate the scenario of a low-altitude robot with a downward-facing camera localizing in an approximately-planar world using a visual map constructed at an altitude several times higher. These experiments were conducted in the Gazebo simulation environment, using a world consisting of a variety publicly-available textured 3D models spread over an un-textured ground plane. A simulated camera was used first to take a ”map” image at a high altitude, encompassing all of the objects in the environment. Then, a set of query images were taken at a ”pyramid” of poses spaced in regular 9-by-9 grids at a range of altitudes, giving rise to scale changes between the map and query image of 1.5 times to 7 times. All images taken were pixels, with a simulated focal length of 1039.47 in pixel units.

At each location, a random orientation was generated by sampling a , , and from a uniform distribution over the range , and these deltas were added to the yaw, pitch, and roll of the downward-facing mapping camera’s orientation. The camera pose from which each image was taken was recorded and used as ground truth for evaluation. Images which did not contain any visible objects were filtered out of the dataset. The final simulated dataset consisted of one map image and 561 query images. Fig.  3 is the map image against which the query images were compared.

For each query image, a set of point matches was produced between the query image and the map image according to the method detailed in  III, or using SIFT feature matching in the case of the SIFT-only experiments. These point matches were used to estimate both a homography matrix and an essential matrix . Pose estimates and describing the transform between the mapping camera and the query camera, consisting of a rotation matrix and a translation vector, was derived from each of these matrices. The reprojection error of all inlier matches for each of and was computed from the respective pose estimates, and the pose estimate with the lower reprojection error was returned. The quality of the estimate was measured as the relative positional error, as defined in  2.

Fig. 3: The map image of the gazebo environment used in our experiments, without noise.

where is the ground-truth translation between the two cameras and is the estimated translation. We normalize the vector from the estimated pose to the true pose to remove the correlation of that vector’s length with the magnitude of the true translation. In degenerate cases of erroneous matches, the estimated translation can be extremely large, and in practice any greater than 2 means that the magnitude of the error is twice the magnitude of the actual translation. Hence, we threshold the error above at 2:


If no pose can be estimated for a query image due to insufficient or incoherent point matches, we set the error to 2 for this query as well. To test the degree of robustness to noise of our technique, each experiment is repeated with multiple levels of Gaussian per-pixel noise applied to each image. The values used are 0, 0.04, 0.08, and 0.12, where the pixel intensities range from 0 to 1. The same per-pixel noise is applied to each colour channel.

Iv-B Results

Table  II displays the average error of each configuration of our system over all queries, as well as that of using SIFT features alone. Features drawn from network layer res5c with -pixel inputs gave the best performance overall, but notably, 13 of the 24 configurations - more than half - outperformed SIFT overall. Notably, for each input resolution, the deepest convolutional layers of the network - res4f, res5c, and pool5 - performed best at each input resolution. This supports the idea that more levels of abstraction from the input pixels in a descriptor leads to improved robustness to scale and perspective change.

Fig.  4 compares the performance of feature res5c at input resolution to that of SIFT features alone, at each query scale factor and at each noise level. All methods have average error close to 2 at scale factors of 5 and above, implying that most queries fail to localize at these scale changes. At scale factors 2, 3, and 4, however, there is a notable difference between our system and SIFT features at all noise levels. The gap between the average error without noise and that with the highest level of noise is significantly greater for SIFT features than for our system at all scale factors less than 4, indicating that limiting the search for SIFT matches only to the regions of matching objects improves their robustness not only to changes in scale, but also to image noise.

Input res. pool1 res2c res3d res4f res5c pool5
1.751 1.889 1.779 1.669 1.609 1.630
1.768 1.842 1.685 1.614 1.591 1.612
1.740 1.750 1.626 1.589 1.587
1.673 1.676 1.619 1.573 1.594 1.604
SIFT 1.666
TABLE II: The average positional error over all query images at all noise levels, for each configuration of our system, as well as for SIFT features alone. The best-performing configuration is in bold.
Fig. 4: The mean error vs. the scale factor relative to the map image for SIFT features and the best configuration of our system, at four levels of per-pixel noise.

V Montreal Image Pair Experiments

V-a Experimental Setup

To test the effectiveness of the proposed system in a real-world scenario, a set of 31 image pairs were taken across twelve scenes surrounding the Montreal campus. Scenes were chosen to contain a roughly-centred object of approximately uniform depth in the scene, so that a uniform change in the object’s image scale could be achieved by taking images at various distances from the object. Scenes were also selected so that any objects surrounding the central object were closer to the camera than the central object, so that no part of the scene would exhibit a much smaller scale change than that of the central object. This ensures that successful matches must be made under the close to the desired change in scale, and makes the relationship between the images amenable to description by a homography. The image pairs exhibit changes in scale ranging from factors of about 1.5 to about 7, with the exception of one image pair showing scale change of about 15 in a prominent foreground object. All images were taken using the rear-facing camera of a Samsung Galaxy S3 phone, and were downsampled to 900x1200 pixels via bilinear interpolation for all experiments.

Each image pair was hand-annotated with a set of 10 point correspondences, distributed approximately evenly over the nearer image in each pair. The proposed system was used to compute point matches between each image pair, and from these point matches, a homography was computed as described in section  III. was used to calculate the total symmetric transfer error (STE) for the image pair over the ground truth points:


The maximum STE we observed for any attempted method on any image pair was . Whenever no could be found for an image pair by some method, its on that image pair was set to . The plain STE ranges over many orders of magnitude on this dataset, so we present the results using the logarithmic STE, making the results easier to interpret.

The same set of parameters were run over this dataset as in the simulation experiments - our system at six network layers and four input resolutions, plus SIFT alone for comparison.

V-B Results

Table  III shows the performance of each feature layer and each input resolution over the whole Montreal dataset, and shows the results from using SIFT features alone as well. As this table shows, the total error using just SIFT features is significantly greater than that of the best-performing input resolution for each feature layer. Also, the average error of the intermediate layers res2c, res3d, and res4f, are all very comparable. It is interesting to note that in this experiment, more intermediate layers are favoured, while the simulation experiments favoured the deepest layers of the network. This may arise from the relatively small degrees of change in viewing angle between these image pairs in comparison with those in the simulation experiments.

Fig  5 show the error of each of the three best-performing configuration, as well as SIFT, on each of the image pairs in the dataset, plotted versus the median scale change over all pairs of ground-truth matches in each image. The scale change between matches is defined in  5. The lines of best fit for each method further emphasize the improvement of our system over SIFT features at all scale factors up to 6. The best-fit lines for all of the top-three configurations of our system overlap almost perfectly, although there is a fair degree of variance in their performances on individual examples.


The use of homographies to relate the image pairs allows us to visually inspect the quality of the estimated , by using to map all pixels in the farther image to their estimated locations in the nearer image. Visual inspection of these mappings for the 31 image pairs confirm that those configurations with lower logarithmic STEs tend to have more correct-looking mappings, although all configurations of our system with mean logarithmic STE ¡ 10 produce comparable mappings for most pairs, and on some pairs, higher-error configurations such as res4f with -pixel inputs produce a subjectively better mapping than the lowest-error configuration. Fig.  1 and Fig.  6 display some example homography mappings.

Input res. pool1 res2c res3d res4f res5c pool5
11.102 13.057 9.631 10.277 10.537 10.011
10.389 11.716 10.231 9.458 9.930 9.505
10.921 9.381 9.339 9.777 10.234 9.667
10.134 10.162 10.473 9.658 10.301 10.607
SIFT 10.654
TABLE III: A table showing the logarithmic STE of each configuration of the system, averaged over all image pairs. The best-performing feature overall is res3d with a input size, followed closely by res2c with inputs and res4f with inputs. The mean log. STE of SIFT features alone is presented as well, for comparison.
Fig. 5: The error of SIFT features alone, and the three best-performing configurations of our system, on each image pair in the dataset, plotted versus the median scale change exhibited in the image pair, along with a line of best fit for each method.
Fig. 6: The results of SIFT features and three different configurations of our system on another pair. The homography estimated by the best-overall configuration, res3d at input size, is notably worse than those produced by two other intermediate feature layers. No configuration of our system performs best on all image pairs.

Vi Conclusions

We have shown that by combining deep learning with classical methods, we can recognize scenes and perform accurate localization across vast changes in scale. Our system uses a pre-trained deep network to describe arbitrary objects and correctly match them between images for use as navigation landmarks. Restricting SIFT feature matching to matched objects substantially improves the robustness of SIFT features both to changes in image noise and to changes in scale. Despite much prior work on place recognition and localization using both classical methods and deep learning, our result sets a new benchmark for metric localization performance across scale.

One strength of our proposed system is that it requires no domain-specific training, making use only of a pre-trained CNN. However, as future work we wish to explore the possibility of training a CNN with the specific objective of producing a perspective-invariant object descriptor, as doing so may result in more accurate matching of objects. We also wish to explore the possibility that including matches from multiple layers of the network in the localization process could improve the system’s accuracy.


  • [1] M. Johnson-Roberson, O. Pizarro, S. B. Williams, and I. Mahon, “Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys,” Journal of Field Robotics, vol. 27, no. 1, pp. 21–51, 2010.
  • [2] J. Sattar, G. Dudek, O. Chiu, I. Rekleitis, P. Giguère, A. Mills, N. Plamondon, C. Prahacs, Y. Girdhar, M. Nahon, and J.-P. Lobos, “Enabling autonomous capabilities in underwater robotics,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Nice, France, September 2008.
  • [3] I. Goodfellow, Y. Bengio, and A. Courville, “Deep learning. book in preparation for mit press,” URL¡ http://www. deeplearningbook. org, 2016.
  • [4] G. Dudek and M. Jenkin, Computational principles of mobile robotics.   Cambridge university press, 2010.
  • [5] J. J. Leonard and H. F. Durrant-Whyte, “Mobile robot localization by tracking geometric beacons,” IEEE Transactions on robotics and Automation, vol. 7, no. 3, pp. 376–382, 1991.
  • [6] P. MacKenzie and G. Dudek, “Precise positioning using model-based maps,” in Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on.   IEEE, 1994, pp. 1615–1621.
  • [7] D. Fox, W. Burgard, and S. Thrun, “Markov localization for mobile robots in dynamic environments,” Journal of Artificial Intelligence Research, vol. 11, pp. 391–427, 1999.
  • [8] A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” Int. J. Comput. Vision, vol. 42, no. 3, pp. 145–175, May 2001. [Online]. Available: http://dx.doi.org/10.1023/A:1011139631724
  • [9] M. Milford and G. Wyeth, “Seqslam : visual route-based navigation for sunny summer days and stormy winter nights,” in IEEE International Conferece on Robotics and Automation (ICRA 2012), N. Papanikolopoulos, Ed.   River Centre, Saint Paul, Minnesota: IEEE, 2012, pp. 1643–1649. [Online]. Available: http://eprints.qut.edu.au/51538/
  • [10] M. Cummins and P. Newman, “Invited Applications Paper FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model,” in 27th Intl Conf. on Machine Learning (ICML2010), 2010.
  • [11] R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós, “Orb-slam: a versatile and accurate monocular slam system.” CoRR, vol. abs/1502.00956, 2015.
  • [12] K. L. Ho and P. Newman, “Detecting loop closure with scene sequences,” Int. J. Comput. Vision, vol. 74, no. 3, pp. 261–286, Sept. 2007. [Online]. Available: http://dx.doi.org/10.1007/s11263-006-0020-1
  • [13] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features (surf),” Comput. Vis. Image Underst., vol. 110, no. 3, pp. 346–359, June 2008. [Online]. Available: http://dx.doi.org/10.1016/j.cviu.2007.09.014
  • [14] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in Proceedings of the 2011 International Conference on Computer Vision, ser. ICCV ’11.   Washington, DC, USA: IEEE Computer Society, 2011, pp. 2564–2571. [Online]. Available: http://dx.doi.org/10.1109/ICCV.2011.6126544
  • [15] D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2, ser. ICCV ’99.   Washington, DC, USA: IEEE Computer Society, 1999, pp. 1150–. [Online]. Available: http://dl.acm.org/citation.cfm?id=850924.851523
  • [16] J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in European Conference on Computer Vision.   Springer, 2014, pp. 834–849.
  • [17] P. Hansen and B. Browning, “Visual place recognition using hmm sequence matching,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 2014, pp. 4549–4555.
  • [18] C. Cadena, D. Galvez-López, J. D. Tardos, and J. Neira, “Robust place recognition with stereo sequences,” IEEE Transactions on Robotics, vol. 28, no. 4, pp. 871–885, Aug 2012.
  • [19] Y. Liu and H. Zhang, “Performance evaluation of whole-image descriptors in visual loop closure detection,” in 2013 IEEE International Conference on Information and Automation (ICIA), Aug 2013, pp. 716–722.
  • [20] T. Naseer, L. Spinello, W. Burgard, and C. Stachniss, “Robust visual robot localization across seasons using network flows,” in AAAI Conference on Artificial Intelligence, 2014. [Online]. Available: http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8483
  • [21] R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. Kelly, and A. J. Davison, “Slam++: Simultaneous localisation and mapping at the level of objects,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 1352–1359.
  • [22] C. Linegar, W. Churchill, and P. Newman, “Made to measure: Bespoke landmarks for 24-hour, all-weather localisation with a camera,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 787–794.
  • [23] R. M. E. Jie Li and M. Johnson-Roberson, “High-level visual features for underwater place recognition.”
  • [24] S. L. Bowman, N. Atanasov, K. Daniilidis, and G. J. Pappas, “Probabilistic data association for semantic slam,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 1722–1729.
  • [25] P. Felzenszwalb, D. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on.   IEEE, 2008, pp. 1–8.
  • [26] A. Kendall and R. Cipolla, “Modelling uncertainty in deep learning for camera relocalization,” Proceedings of the International Conference on Robotics and Automation (ICRA), 2016.
  • [27] N. Sünderhauf, F. Dayoub, S. Shirazi, B. Upcroft, and M. Milford, “On the performance of convnet features for place recognition,” CoRR, vol. abs/1501.04158, 2015. [Online]. Available: http://arxiv.org/abs/1501.04158
  • [28] A. J. F. D. E. P. B. U. Niko S Ìˆunderhauf, Sareh Shirazi and M. Milford, “Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free,” in Proceedings of Robotics: Science and Systems (RSS), 2015.
  • [29] T. Schmidt, R. Newcombe, and D. Fox, “Self-supervised visual descriptor learning for dense correspondence,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 420–427, 2017.
  • [30] E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer, “Discriminative learning of deep convolutional feature point descriptors,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 118–126.
  • [31] J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, “Selective search for object recognition,” International Journal of Computer Vision, vol. 104, no. 2, pp. 154–171, 2013. [Online]. Available: https://ivi.fnwi.uva.nl/isis/publications/2013/UijlingsIJCV2013
  • [32] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385, 2015.
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