Advanced Robotics

Li, Xiuzhi; Wang, Wen; Chen, Jiahao; Zhang, Xiangyin

doi: 10.1080/01691864.2022.2129032pmid: N/A

In this paper, a drift rejection SLAM (Simultaneous Localization and Mapping) method is proposed targeting indoor scenarios, where SLAM generates large drifts due to the lack of reliable features. To provide sufficient features, we leverage multiple feature primitives and geometric restraints (parallel or perpendicular) restraint in man-made environments. Under some satisfy Manhattan World (MW) assumption scene, such as corridors, we can get absolute and drift-free rotation estimation using a Gaussian sphere. By fully utilizing drift-free rotation estimation under MW assumption and the local stability of purely track restricted by point, line, and plane features, our drift rejection SLAM method becomes more accurate and robust. Additionally, by exploiting the constraint of planar motion on ground robot, we propose an ingenious strategy to reduce translation drift by eliminating vertical movement in the Manhattan world. Advantages of our method over other state-of-the-art algorithms are validated on public datasets and real-world experiments. The code is released at https://github.com/WangWen-Believer/DR-SLAM.

Zhou, Benchun; Gilles, Maximilian; Meng, Yongqi

doi: 10.1080/01691864.2022.2123253pmid: N/A

Simultaneous localization and mapping (SLAM) is a fundamental problem for indoor mobile robots operating in unknown environments. While visual SLAM systems often use geometry features, the reconstructed maps lack semantic information. On the other hand, current object detection methods provide rich information about the scene from the image. In this paper, we present a structure SLAM system with feature points, geometry planes, and semantic objects. Unlike other systems modeling planes and objects as collections of points, we choose a parametric representation for these landmarks. For every single frame, we start by generating cuboid candidates of detected objects with varying dimensions and orientations, then use 2D-3D fitting constraints to calculate the cuboid's translation, and finally introduce 3D spatial and 2D image constraints to select the best cuboid candidate. For SLAM optimization, the detected planes and objects can provide geometry constraints to improve the localization result, and act as landmarks to reconstruct a semantic map. Experiments on the ICL NUIM RGB-D dataset show that the proposed point-plane-object SLAM system can slightly improve localization accuracy, and is able to build a semantic map of the environment.

Arthur, Jasper; Khoshdarregi, Matt

doi: 10.1080/01691864.2022.2125828pmid: N/A

Industrial robots have been increasingly adopted in precision manufacturing applications such as aerospace drilling. However, achieving the strict tolerance requirements of the aerospace industry has been a major challenge due to the relatively poor accuracy of robots. One of the major sources of error which has a detrimental effect on the quality and circularity of drilled holes is the static friction in robot joints. These errors are particularly pronounced when one or more joints reverse direction. To improve robot motion for better hole quality, this paper proposes an optimization framework to eliminate or minimize joint reversals throughout a drilling motion. A general robotic drilling motion with a redundant degree of freedom due to the twist of the tool is first modeled. Particle Swarm Optimization (PSO) is then used for strategic pose selection considering the entire drilling motion. Experimental tests performed on a KUKA KR 6 R700-2 show a 40% reduction in the tool deviation envelope. The proposed technique can be readily implemented on any commercial robotic drilling cell without interfering with the controller.

Yem, Vibol; Nashiki, Reon; Morita, Tsubasa; Ikei, Yasushi

doi: 10.1080/01691864.2022.2129033pmid: N/A

In this study, using a personal vehicle (i.e. Segway) and a wheelchair-type motion display, we proposed a vehicle-ride sensation sharing system to enable a local rider to collaborate with a remote driver immersively. The local rider sitting in the motion display can receive both the 3D visual perception and the vibro-vestibular sensation. The remote driver side of the system was developed by attaching the Segway with two 360-degree cameras and a stabilizer to capture stereoscopic 3D images and send them to each eye of a head-mounted display worn by a local rider. By modifying a conventional wheelchair with a simple, lightweight mechanism for actuation and vibration by two DC motors, we developed the prototype of the vibro-vestibular display for local riders. Then, we investigated the effectiveness of a vibro-vestibular wheelchair. The result showed that the acceleration/deceleration of the wheelchair proportional to that of visual cue could significantly reduce virtual reality (VR) sickness by approximately 54% and increase the sense of riding a vehicle by approximately 2.25 times. Moreover, we conducted a demo experience in SIGGRAPH ASIA 2019 for 3 days and 89 participants filled the questionnaire related to our system validation. The results suggested that vibro-vestibular feedback by the wheelchair is important for remote collaboration that uses a mobile vehicle.