Pose interpolation SLAM for large maps using moving 3D sensors

Ceriani, Simone; Sánchez, Carlos; Taddei, Pierluigi; Wolfart, Erik; Vitor, Sequeira

doi:10.1109/iros.2015.7353456

Cited by 30 publications

(17 citation statements)

References 16 publications

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“…When the scanning rate is slow, a scan cannot be consider as a rigid body but distortion is present due to external motion of the laser scanner. To date, it has been shown that motion can be recovered with a laser scanner itself (Ceriani, Sanchez, Taddei, Wolfart, & Sequeira, ; Velas, Spanel, & Herout, ; Wei, Wu, & Fu, ). This requires a motion model being involved.…”

Section: Related Workmentioning

confidence: 99%

Laser–visual–inertial odometry and mapping with high robustness and low drift

Zhang¹,

Singh²

2018

Journal of Field Robotics

188

115

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We present a data processing pipeline to online estimate ego-motion and build a map of the traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). Different from traditional methods that use a Kalman filter or factor-graph optimization, the proposed method employs a sequential, multilayer processing pipeline, solving for motion from coarse to fine.Starting with IMU mechanization for motion prediction, a visual-inertial coupled method estimates motion; then, a scan matching method further refines the motion estimates and registers maps. The resulting system enables high-frequency, lowlatency ego-motion estimation, along with dense, accurate 3D map registration.Further, the method is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in the dark, texture-less, and structure-less environments. During experiments, the method demonstrates 0.22% of relative position drift over 9.3 km of navigation and robustness w.r.t. running, jumping, and even highway speed driving (up to 33 m/s).mapping, ego-motion estimation, 3D laser scanner, vision, inertial sensor 1 | INTRODUCTION This paper aims at developing a method for online ego-motion estimation with data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). The estimated motion further registers laser points to build a map of the traversed environment. In many real-world applications, ego-motion estimation and mapping must be conducted in real time. In an autonomous navigation system, the map is crucial for motion planning and obstacle avoidance, while the motion estimation is important for vehicle control and maneuver.Very often, high-accuracy global positioning system (GPS)-inertial navigation system (INS) solutions are impractical when the application is GPS-denied, lightweight, or cost-sensitive. The proposed method utilizes only perception sensors without reliance on GPS.Specially, we are interested in solving for extremely aggressive motion. Yet, it remains nonobvious how to solve the problem reliably in 6 degrees of freedom (DOF), in real time, and in a small form factor. The problem is closely relevant to sensor degradation due to sparsity of the data during dynamic maneuver. To the best of our knowledge, the proposed method is by far the first to enable such high-rate ego-motion estimation capable of handling running and jumping (see Figure 1 as an example), while at the same time develop a dense, accurate 3D map, in the field under various lighting and structural conditions, and using only sensing and computing devices that can be easily carried by a person.The key reason that enables this level of performance is our novel way of data processing. As shown in Figure 2a, a standard Kalman filter-based method typically uses IMU mechanization in a prediction step followed by update steps seeded with individual visual features or laser landmarks. On the other hand, a fa...

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Section: Related Workmentioning

confidence: 99%

Laser–visual–inertial odometry and mapping with high robustness and low drift

Zhang¹,

Singh²

2018

Journal of Field Robotics

188

115

View full text Add to dashboard Cite

show abstract

“…Current state-of-the-art SLAM systems using just laser scanner are [19]- [23], in which a motion model is required, either a constant velocity model or a Gaussian process. Approach in [24] combines stereo cameras and a laser scanner.…”

Section: Related Workmentioning

confidence: 99%

Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping

Shao

Vijayarangan

Liu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VIL-SLAM accomplishes this by incorporating tightly-coupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loop-closure corrected 6-DOF LiDAR poses in real-time and 1cm voxel dense maps near real-time. VIL-SLAM demonstrates improved accuracy and robustness compared to state-of-the-art LiDAR methods.

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“…Main benefits are that (a) surfaces missing in the reference map can be exploited during the registration process and that (b) the system allows exploring non-mapped areas by continuously tracking the user. In this latter case the system corresponds to the local tracking described by Ceriani et al [5] .…”

Section: Relative Tracker Integrationmentioning

confidence: 99%

“…For instance, the environment in case B has big windows that allow the sensor to acquire points from the outside, which are not present in the original map. For a drift analysis of the relative tracker alone the reader may refer to [5] .…”

Section: Relative Tracker Integrationmentioning

confidence: 99%

Localization and tracking in known large environments using portable real-time 3D sensors

Sánchez

Taddei

Ceriani

et al. 2016

Computer Vision and Image Understanding

Self Cite

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a b s t r a c tEgo-motion estimation and localization in large environments are key components in any assistive technology for real-time user orientation and navigation. We consider the case where a large known environment is explored without a priori assumptions on the initial location. In particular we propose a framework that uses a single portable 3D sensor to solve the place recognition problem and continuously tracks its position even when leaving the known area or when significant changes occur in the observed environment.We cast the place recognition step as a classification problem and propose an efficient search space reduction considering only navigable areas where the user can be localized. Classification hypotheses are then discarded exploiting temporal consistency w.r.t. a relative tracker that exploits only the sensor input data. The solution uses a compact classifier whose representation scales well with the map size. After being localized, the user is continuously tracked exploiting the known environment using an efficient data structure that provides constant access time for nearest neighbor searches and that can be streamed to keep only the local region close to the last known position in memory. Robust results are achieved by performing a geometrically stable selection of points, efficiently filtering outliers and integrating the relative tracker based on previous observations. We experimentally show that such a framework provides good localization results and that it scales well with the environment map size yielding real-time performance for both place recognition and tracking.

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Pose interpolation SLAM for large maps using moving 3D sensors

Cited by 30 publications

References 16 publications

Laser–visual–inertial odometry and mapping with high robustness and low drift

Laser–visual–inertial odometry and mapping with high robustness and low drift

Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping

Localization and tracking in known large environments using portable real-time 3D sensors

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