Towards semantic SLAM using a monocular camera

Civera, Javier; Gálvez-López, Dorian; Riazuelo, Luis; Tardós, Juan D.; Montiel, José María

doi:10.1109/iros.2011.6094648

Cited by 162 publications

(84 citation statements)

References 25 publications

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“…Depending on whether the measurement z k s,t is a 1 or a 0, the likelihood function derived in (14) takes on a different shape. The negative log likelihood function of (4) shown in Fig.…”

Section: Maximum-likelihood Formulation With Semantic Datamentioning

confidence: 99%

Robust Estimation Framework with Semantic Measurements

Cai

Harvard

Murray

et al. 2019

2019 American Control Conference (ACC)

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Conventional simultaneous localization and mapping (SLAM) algorithms rely on geometric measurements and require loop-closure detections to correct for drift accumulated over a vehicle trajectory. Semantic measurements can add measurement redundancy and provide an alternative form of loop closure. We propose two different estimation algorithms that incorporate semantic measurements provided by visionbased object classifiers. An a priori map of regions where the objects can be detected is assumed. The first estimation framework is posed as a maximum-likelihood problem, where the likelihood function for semantic measurements is derived from the confusion matrices of the object classifiers. The second estimation framework is comprised of two parts: 1) a continuous-state estimation formulation that includes semantic measurements as a form of state constraints and 2) a discretestate estimation formulation used to compute the certainty of object detection measurements using a Hidden Markov Model (HMM). The advantages of incorporating semantic measurements in these frameworks are demonstrated in numerical simulations. In particular, the proposed estimation algorithms improve upon the robustness and accuracy of conventional SLAM algorithms.

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“…Depending on whether the measurement z k s,t is a 1 or a 0, the likelihood function derived in (14) takes on a different shape. The negative log likelihood function of (4) shown in Fig.…”

Section: Maximum-likelihood Formulation With Semantic Datamentioning

confidence: 99%

Robust Estimation Framework with Semantic Measurements

Cai

Harvard

Murray

et al. 2019

2019 American Control Conference (ACC)

View full text Add to dashboard Cite

show abstract

“…Civera et al proposed a semantic SLAM using a monocular extended kalman filter (EKF) SLAM and inserted 3-D objects into geometric map. 35 Anand et al trained a graphical model for contextually guided semantic labeling. 36 Yang et al proposed a method to solve navigation and vehicle distance estimation simultaneously and used dynamic object tracking to divide view field of camera into static and dynamic parts.…”

Section: Related Workmentioning

confidence: 99%

Semantic segmentation–aided visual odometry for urban autonomous driving

Zhang

Gao

et al. 2017

International Journal of Advanced Robotic Systems

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Visual odometry plays an important role in urban autonomous driving cars. Feature-based visual odometry methods sample the candidates randomly from all available feature points, while alignment-based visual odometry methods take all pixels into account. These methods hold an assumption that quantitative majority of candidate visual cues could represent the truth of motions. But in real urban traffic scenes, this assumption could be broken by lots of dynamic traffic participants. Big trucks or buses may occupy the main image parts of a front-view monocular camera and result in wrong visual odometry estimation. Finding available visual cues that could represent real motion is the most important and hardest step for visual odometry in the dynamic environment. Semantic attributes of pixels could be considered as a more reasonable factor for candidate selection in that case. This article analyzed the availability of all visual cues with the help of pixel-level semantic information and proposed a new visual odometry method that combines feature-based and alignment-based visual odometry methods with one optimization pipeline. The proposed method was compared with three open-source visual odometry algorithms on Kitti benchmark data sets and our own data set. Experimental results confirmed that the new approach provided effective improvement both on accurate and robustness in the complex dynamic scenes.

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“…Their system is a very good option for post-processing but hardly applicable in online robotic operation. An online SLAM system recognizing objects is proposed by [7], where in parallel to a monocular EKF SLAM thread there is a thread comparing visual features with the visual features of objects in the database. The recognition thread is always comparing against all the specific instances and as such is not scalable in the number of different objects and instances.…”

Section: Related Workmentioning

confidence: 99%

Semantic parsing for priming object detection in indoors RGB-D scenes

Cadena

Košecká

2014

The International Journal of Robotics Research

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The semantic mapping of the environment requires simultaneous segmentation and categorization of the acquired stream of sensory information. The existing methods typically consider the semantic mapping as the final goal and differ in the number and types of considered semantic categories. We envision semantic understanding of the environment as an on-going process and seek representations which can be refined and adapted depending on the task and robot's interaction with the environment. In this work we propose a novel and efficient method for semantic parsing, which can be adopted to the task at hand and enables localization of objects of interest in indoors environments. For basic mobility tasks we demonstrate how to obtain initial semantic segmentation of the scene into ground, structure, furniture and props categories which constitute the first level of hierarchy. Then, we propose a simple and efficient method for predicting locations of objects that based on their size afford a manipulation task. In our experiments we use the publicly available NYU V2 dataset [35] and obtain better or comparable results than the state of the art at the fraction of computational cost. We show the generalization of our approach on two more publicly available datasets.

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Towards semantic SLAM using a monocular camera

Cited by 162 publications

References 25 publications

Robust Estimation Framework with Semantic Measurements

Robust Estimation Framework with Semantic Measurements

Semantic segmentation–aided visual odometry for urban autonomous driving

Semantic parsing for priming object detection in indoors RGB-D scenes

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