Target localization using RGB-D camera and LiDAR sensor fusion for relative navigation

Song, Hyoung Woon; Choi, Won-Sub; Lim, Seongmin; Kim, Hae-Dong

doi:10.1109/cacs.2014.7097178

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…These studies can be grouped depending on their application areas and 3D modeling techniques. Some of the applications of 3D modeling presented in the literature include 3D building modeling [28], 3D city modeling [29], automatic registration for georeferencing [30], railroad center line reconstruction [31], automatic building extraction [32], scene parsing [33], elevation mapping [34], species recognition, height and crown width estimation [35], target localization and relative navigation [36], and visual localization [37]. Modeling techniques used in these studies involve automatic aerial triangulation, coarseto-fine methods [28], digital surface nodes application [29], the iterative closest-point (ICP) algorithm [30], the random sample consensus algorithm [31], the binary space partitioning (BSP) tree [32], the Markov-random-field-based temporal method [33], fuzzy logic and particles [34], multi-scale template matching (MSTM) [35], dynamic bias estimation [36], and localization-by-recognition and vocabulary-tree-based recognition methods [37].…”

Section: Related Workmentioning

confidence: 99%

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Smith¹,

Sevil²

2022

Robotics

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The aim of this research effort was to develop a framework for a structure from motion (SfM)-based 3D reconstruction approach with a team of autonomous small unmanned aerial systems (sUASs) using a distributed behavior model. The framework is composed of two major goals to accomplish this: a distributed behavior model for a team of sUASs and a SfM-based 3D reconstruction using team of sUASs. The developed distributed behavior model is based on the entropy of the system, and when the entropy of the system is high, the sUASs get closer to reducing the overall entropy. This is called the grouping phase. If the entropy is less than the predefined threshold, then the sUASs switch to the 3D reconstruction phase. The novel part of the framework is that sUASs are only given the object of interest to reconstruct the 3D model, and they use the developed distributed behavior to coordinate their motion for that goal. A comprehensive parameter analysis was performed, and optimum sets of parameters were selected for each sub-system. Finally, optimum parameters for two sub-systems were combined in a simulation to demonstrate the framework’s operability and evaluate the completeness and speed of the reconstructed model. The simulation results show that the framework operates successfully and is capable of generating complete models as desired, autonomously.

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Section: Related Workmentioning

confidence: 99%

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Smith¹,

Sevil²

2022

Robotics

View full text Add to dashboard Cite

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“…Ref. [17] describes an approach to estimate the position of targets based on fusion of RGB-D camera and 2D LiDAR sensor measurements. Other authors [18,19] demonstrate instead the effectiveness of an RGB-D camera for obstacle avoidance tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Sensors that are most frequently used (i.e., [17][18][19]) are the Intel RealSense [20] and the Kinect [21], both characterized by a limited depth range and field of view. Thus, in addition to the local path planning algorithm strategies listed in [7,10], there is also a new category that is gaining momentum in local navigation and it is associated with the field of collision avoidance with limited field of view sensing [3][4][5].…”

Section: Related Workmentioning

confidence: 99%

Active Exploration for Obstacle Detection on a Mobile Humanoid Robot

et al. 2021

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Conventional approaches to robot navigation in unstructured environments rely on information acquired from the LiDAR mounted on the robot base to detect and avoid obstacles. This approach fails to detect obstacles that are too small, or that are invisible because they are outside the LiDAR’s field of view. A possible strategy is to integrate information from other sensors. In this paper, we explore the possibility of using depth information from a movable RGB-D camera mounted on the head of the robot, and investigate, in particular, active control strategies to effectively scan the environment. Existing works combine RGBD-D and 2D LiDAR data passively by fusing the current point-cloud from the RGB-D camera with the occupancy grid computed from the 2D LiDAR data, while the robot follows a given path. In contrast, we propose an optimization strategy that actively changes the position of the robot’s head, where the camera is mounted, at each point of the given navigation path; thus, we can fully exploit the RGB-D camera to detect, and hence avoid, obstacles undetected by the 2D LiDAR, such as overhanging obstacles or obstacles in blind spots. We validate our approach in both simulation environments to gather statistically significant data and real environments to show the applicability of our method to real robots. The platform used is the humanoid robot R1.

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“…5 One can certainly imagine scenarios where combinations of these sensors would allow for more robust and accurate estimations of range (or depth) to surrounding objects. [6][7][8] Without loss of generalization, we will focus on the depth measurements obtained from a LIDAR, such as the Velodyne or Slamtech LIDAR, in this paper. The experiments we conduct in our investigations are based on indoor experiments with a dual-differential drive robot with a Slamtech RPLidar S1.…”

Section: Introductionmentioning

confidence: 99%

Comparing performance of robot operating system (ROS) mapping algorithms in the presence of degraded or obscured depth sensors

Senczyszyn,

Pinar,

Salem

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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Safety and robust operation of autonomous vehicles is a pertinent concern for both defense and also civilian systems. From self-driving cars to autonomous Navy vessels and Army vehicles, malfunctions can have devastating consequences, including losses of life and infrastructure. Autonomous ground vehicles use a variety of sensors to image their environment: passive sensors such as RGB and thermal cameras, and active sensors such as RGBD, LIDAR, radar, and sonar. These sensors are either used alone or fused to accomplish the basic mobile autonomy tasks: obstacle avoidance, localization, mapping, and, subsequently, path-planning. In this paper, we will provide a qualitative and quantitative analysis of the limitations of ROS mapping algorithms when depth sensors-LIDAR and RGBD-are degraded or obscured, e.g., by dust, heavy rain, snow, or other noise. Aspects that will be investigated include form of the degradation and effect on the autonomous operations. This work will summarize the limitations of publicly available ROS mapping algorithms when depth sensors are degraded or obscured. Furthermore, this work will lay a foundation for developing robust autonomy algorithms that are resilient to possible degraded or obscured sensors.

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Target localization using RGB-D camera and LiDAR sensor fusion for relative navigation

Cited by 10 publications

References 13 publications

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Active Exploration for Obstacle Detection on a Mobile Humanoid Robot

Comparing performance of robot operating system (ROS) mapping algorithms in the presence of degraded or obscured depth sensors

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