We demonstrate the use of semantic object detections as robust features for Visual Teach and Repeat (VTR). Recent CNN-based object detectors are able to reliably detect objects of tens or hundreds of categories in a video at frame rates. We show that such detections are repeatable enough to use as landmarks for VTR, without any low-level image features. Since object detections are highly invariant to lighting and surface appearance changes, our VTR can cope with global lighting changes and local movements of the landmark objects. In the teaching phase, we build a series of compact scene descriptors: a list of detected object labels and their image-plane locations. In the repeating phase, we use Seq-SLAM-like relocalization to identify the most similar learned scene, then use a motion control algorithm based on the funnel lane theory to navigate the robot along the previously piloted trajectory. We evaluate the method on a commodity UAV, examining the robustness of the algorithm to new viewpoints, lighting conditions, and movements of landmark objects. The results suggest that semantic object features could be useful due to their invariance to superficial appearance changes compared to low-level image features.
Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand, object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks, ideal for view-independent unambiguous loop closure. In a map with several objects of the same class, however, a crucial data association problem exists. While data association and recognition are discrete problems usually solved using discrete inference, classical SLAM is a continuous optimization over metric information. In this paper, we formulate an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decompose it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states. The estimated landmark and robot poses affect the association and class distributions, which in turn affect the robot-landmark pose optimization. The performance of our algorithm is demonstrated on indoor and outdoor datasets. Vision-based, route-following algorithms enable autonomous robots to repeat manually taught paths over long distances using inexpensive vision sensors. However, these methods struggle with long-term, outdoor operation due to the challenges of environmental appearance change caused by lighting, weather, and seasons. While techniques exist to address appearance change by using multiple experiences over different environmental conditions, they either provide topological-only localization, require several manually taught experiences in different conditions, or require extensive offline mapping to produce metric localization. For real-world use, we would like to localize metrically to a single manually taught route and gather additional visual experiences during autonomous operations. Accordingly, we propose a novel multi-experience localization (MEL) algorithm developed specifically for route-following applications; it provides continuous, six-degree-of-freedom (6DoF) localization with relative uncertainty to a privileged (manually taught) path using several experiences simultaneously. We validate our algorithm through two experiments: i) an offline performance analysis on a 9km subset of a challenging 27km route-traversal dataset and ii) an online field trial where we demonstrate autonomy on a small 250m loop over the course of a sunny day. Both exhibit significant appearance change due to lighting variation. Through these experiments we show that safe localization can be achieved by bridging the appearance gap. Visual Teach and Repeat VT&R allows an autonomous vehicle to accurately repeat a previously traversed route using only vision sensors. Most VT&R systems rely on natively three-dimensional 3D sensors such as stereo cameras for mapping and localization, but many existing mobile robots are equipped with only 2D monocular vision, typically for teleoperation. In this paper, we extend VT&R to the most basic sensor configuration-a single monocular camera. We show that kilometer-scale route repetition can be achieved with centimeter-level accuracy by approximating the local ground surface near the vehicle as a plane with some uncertainty. This allows our system to recover absolute scale from the known position and orientation of the camera relative to the vehicle, which simplifies threshold-based outlier rejection and the estimation and control of lateral path-tracking error-essential components of high-accuracy route repetition. We enhance the robustness of our monocular VT&R system to common failure cases through the use of color-constant imagery, which provides it with a degree of resistance to lighting changes and moving shadows where keypoint matching on standard gray images tends to struggle. Through extensive testing on a combined 30i¾?km of autonomous navigation data collected on multiple vehicles in a variety of highly nonplanar terrestrial and planetary-analogue environments, we demonstrate that our system is capable of achieving route-repetition accuracy on par with its stereo counterpart, with only a modest tradeoff in robustness. We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scale- and rotation- invariance of the standard feature extractors is less important than their robustness to the mid- and long-term environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG CNN and the STAR GRIEF feature, which was slightly less robust, but faster to calculate. We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, real-time 3D object recognition and tracking provides 6DoF camera-object constraints which feed into an explicit graph of objects, continually refined by efficient pose-graph optimisation. This offers the descriptive and predictive power of SLAM systems which perform dense surface reconstruction, but with a huge representation compression. The object graph enables predictions for accurate ICP-based camera to model tracking at each live frame, and efficient active search for new objects in currently undescribed image regions. We demonstrate real-time incremental SLAM in large, cluttered environments, including loop closure, relocalisation and the detection of moved objects, and of course the generation of an object level scene description with the potential to enable interaction. Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous navigation system that enables the rapid construction of route networks, safely built through operator-controlled driving. Added routes can be followed autonomously using visual localization. To enable long-term operation that is robust to appearance change, its Multi-Experience Localization (MEL) leverages many previously driven experiences when localizing to the manually taught network. While this multi-experience method is effective across appearance change, the computation becomes intractable as the number of experiences grows into the tens and hundreds. This paper introduces an algorithm that prioritizes experiences most relevant to live operation, limiting the number of experiences required for localization. The proposed algorithm uses a visual Bag-of-Words description of the live view to select relevant experiences based on what the vehicle is seeing right now, without having to factor in all possible environmental influences on scene appearance. This system runs in the loop, in real time, does not require bootstrapping, can be applied to any pointfeature MEL paradigm, and eliminates the need for visual training using an online, local visual vocabulary. By picking a subset of visually similar experiences to the live view, we demonstrate safe, vision-in-the-loop route following over a 31 hour period, despite appearance as different as night and day. Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the environment solely for the robot's benefit. Human environments contain many objects which can serve as suitable landmarks for robot navigation such as signs, objects, and furniture. Maps based on high level features which are identified by a learned classifier could better inform tasks such as semantic mapping and mobile manipulation. In this paper we present a technique for recognizing door signs using a learned classifier as one example of this approach, and demonstrate their use in a graphical SLAM framework with data association provided by reasoning about the semantic meaning of the sign. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully-convolutional network that simultaneously predicts object bounds and objectness scores at each position. RPNs are trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. With a simple alternating optimization, RPN and Fast R-CNN can be trained to share convolutional features. For the very deep VGG-16 model [19], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007 (73.2 mAP) and 2012 (70.4 mAP) using 300 proposals per image. Code is available at https: github.com ShaoqingRen faster_rcnn. Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The and (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other. We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prior work. The improved model, YOLOv2, is state-of-the-art on standard detection tasks like PASCAL VOC and COCO. At 67 FPS, YOLOv2 gets 76.8 mAP on VOC 2007. At 40 FPS, YOLOv2 gets 78.6 mAP, outperforming state-of-the-art methods like Faster RCNN with ResNet and SSD while still running significantly faster. Finally we propose a method to jointly train on object detection and classification. Using this method we train YOLO9000 simultaneously on the COCO detection dataset and the ImageNet classification dataset. Our joint training allows YOLO9000 to predict detections for object classes that don't have labelled detection data. We validate our approach on the ImageNet detection task. YOLO9000 gets 19.7 mAP on the ImageNet detection validation set despite only having detection data for 44 of the 200 classes. On the 156 classes not in COCO, YOLO9000 gets 16.0 mAP. But YOLO can detect more than just 200 classes; it predicts detections for more than 9000 different object categories. And it still runs in real-time. We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For (300 300 ) input, SSD achieves 74.3 mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for (512 512 ) input, SSD achieves 76.9 mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https: github.com weiliu89 caffe tree ssd. Long-term visual SLAM, in familiar, semi-dynamic, and partially changing environments is an important area of research in robotics. The main problem we faced is the question of how to describe a scene discriminatively and compactly-both of which are necessary in order to cope with changes in appearance and a large amount of visual information. In this study, we address the above issues by mining visual experience. Our strategy is to mine a library of raw visual images, termed visual experience, to find the relevant visual patterns to effectively explain the input scene. From a practical point of view, our work offers three main contributions over the previous work. First, it is the first application of discriminative visual features from deep convolutional neural networks (DCNN) to the task of visual landmark mining. Second, we show how to interpret a high-dimensional DCNN feature to a compact semantic representation of visual word. Third, we show that our approach can turn the scene description task with any feature (including the DCNN feature) into the task of mining visual experience. Experiments on a challenging cross-domain visual place recognition validate efficacy of the proposed approach. In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot's behaviour during navigation tasks. The system is made available to the community as a ROS module.