SkiMap++: Real-Time Mapping and Object Recognition for Robotics

Gregorio, Daniele De; Cavallari, Tommaso; Stefano, Luigi Di

doi:10.1109/iccvw.2017.84

Cited by 10 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach models the detected semantic classes with a constant Kalman filter module to track and update the most probable object position. Gregorio et al [4] proposed a real-time mapping framework for robot navigation. This work can obtain the objects semantics as well as the 6 DOF poses of object instances in environments by means of object detection and visual features matching.…”

Section: Object-augmented Mappingmentioning

confidence: 99%

Monocular camera and laser based semantic mapping system with temporal-spatial data association for indoor mobile robots

Zuo

Xuan

Zhou

2023

Multimed Tools Appl

View full text Add to dashboard Cite

In the future, the goal of service robots is to operate in human-centric indoor environments, requiring close cooperation with humans. In order to enable the robot to perform various interactive tasks, it is necessary for robots to perceive and understand environments from a human perspective. Semantic map is an augmented representation of the environment, containing both geometric information and high-level qualitative features. It can help the robot to comprehensively understand the environment and bridge the gap in human-robot interaction. In this paper, we propose a unified semantic mapping system for indoor mobile robots. This system utilizes the techniques of scene classification and object detection to construct semantic representations of indoor environments by fusing the data of a camera and a laser. In order to improve the accuracy of semantic mapping, the temporal-spatial correlation of semantics is leveraged to realize data association of semantic maps. Also, the proposed semantic mapping system is scalable and portable, which can be applied to different indoor scenarios. The proposed system was evaluated with collected datasets captured in indoor environments. Extensive experimental results indicate that the proposed semantic mapping system exhibits great performance in the robustness and accuracy of semantic mapping.

show abstract

Section: Object-augmented Mappingmentioning

confidence: 99%

Monocular camera and laser based semantic mapping system with temporal-spatial data association for indoor mobile robots

Zuo

Xuan

Zhou

2023

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…However, the methods proposed above only estimate the location of the target object without orientation. Instead, [4] predicts both the location and the pose of objects by registering 2D feature descriptors to 3D voxel in the object frame. Instead of generating a map with discrete object information, [8] uses Convolutional Neural Networks (CNNs) for prediction and generates a map labelling the entire scene, which also includes walls, floors, doors, etc.. Another approach for generating a rich information map is converting a set of existing individual datasets to one map that contains information about the objects offline [9].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, by unifying SLAM with object recognition, we propose a method that generates a rich information map which represents not only the free space for robot navigation but also the location and orientation information for objects of interest for the operator. This approach is also known as semantic SLAM [4].…”

Section: Introductionmentioning

confidence: 99%

“…1 Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA wang.mao@husky.neu.edu 2 Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA In this paper, by unifying SLAM with object recognition, we propose a method that generates a rich information map which represents not only the free space for robot navigation but also the location and orientation information for objects of interest for the operator. This approach is also known as semantic SLAM [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

Wang

Long

Chang

et al. 2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

View full text Add to dashboard Cite

This paper presents our approach to develop a method for an unmanned ground vehicle (UGV) to perform inspection tasks in nuclear environments using rich information maps. To reduce inspectors exposure to elevated radiation levels, an autonomous navigation framework for the UGV has been developed to perform routine inspections such as counting containers, recording their ID tags and performing gamma measurements on some of them. In order to achieve autonomy, a rich information map is generated which includes not only the 2D global cost map consisting of obstacle locations for path planning, but also the location and orientation information for the objects of interest from the inspector's perspective. The UGV's autonomy framework utilizes this information to prioritize locations to navigate to perform the inspections. In this paper, we present our method of generating this rich information map, originally developed to meet the requirements of the International Atomic Energy Agency (IAEA) Robotics Challenge. We demonstrate the performance of our method in a simulated testbed environment containing uranium hexafluoride (UF6) storage container mock ups.

show abstract

“…A combination of a 2D laser and a pan-tilt unit is used to create 3D map, but this system spends much time to move the pan-tilt unit. 29 There are a lot of research on 3D map building based on object detection, 30 but few research on 3D map building on object contour. It is hard to obtain correct 3D object information only based on object detection boxes.…”

Section: Introductionmentioning

confidence: 99%

3D map building based on extrinsic sensor calibration method and object contour detector with a fully convolutional neural network

Dai

Lee

2022

Measurement and Control

View full text Add to dashboard Cite

In this paper, we propose a simple method to obtain an object’s 3D coordinate information in one image, using one monocular camera and two 2D LiDAR sensors, which are widely used low-cost sensors. An extrinsic calibration method is used for each LiDAR sensor to transfer LiDAR coordinate to camera pixel coordinate in one image. The proportion factor [Formula: see text] is calculated, based on the relationship of pixel from calibration points and distance from detected points of two LiDAR sensors. In order to create a correct 3D map, a deep learning algorithm for real-time object contour detection with a fully Convolutional Neural Network is proposed, through gathering data, labeling data, training model, and testing object contour detector. For 3D map building, by the proportion factor [Formula: see text] and the detector contour feature pixels, the height of the object is obtained. Combining with LiDAR sensor’s detection information, the 3D posture of the object is obtained. Based on the proposed extrinsic sensor calibration method and the object contour detect result, virtual points of the object are calculated. Finally, the experiment results show the 3D coordinate information of the detected single or multiple objects. Assembling sensors and a controller on a Kobuki robot, the indoor 3D map is built based on the proposed methods.

show abstract

SkiMap++: Real-Time Mapping and Object Recognition for Robotics

Cited by 10 publications

References 27 publications

Monocular camera and laser based semantic mapping system with temporal-spatial data association for indoor mobile robots

Monocular camera and laser based semantic mapping system with temporal-spatial data association for indoor mobile robots

Autonomous Robot Navigation with Rich Information Mapping in Nuclear Storage Environments

3D map building based on extrinsic sensor calibration method and object contour detector with a fully convolutional neural network

Contact Info

Product

Resources

About