Pose Interpolation for Laser‐based Visual Odometry

Tong, Chi Hay; Anderson, Sean; Dong, Hang; Barfoot, Timothy D.

doi:10.1002/rob.21537

Cited by 24 publications

(11 citation statements)

References 58 publications

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“…For example, Tong et al match visual features in intensity images created by stacking laser scans from a 2-axis lidar to solve for the motion [16]. The motion is modeled with constant velocity and Gaussian processes.…”

Section: Related Workmentioning

confidence: 99%

Visual-lidar odometry and mapping: low-drift, robust, and fast

Zhang

Singh

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

628

300

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Abstract-Here, we present a general framework for combining visual odometry and lidar odometry in a fundamental and first principle method. The method shows improvements in performance over the state of the art, particularly in robustness to aggressive motion and temporary lack of visual features. The proposed on-line method starts with visual odometry to estimate the ego-motion and to register point clouds from a scanning lidar at a high frequency but low fidelity. Then, scan matching based lidar odometry refines the motion estimation and point cloud registration simultaneously. We show results with datasets collected in our own experiments as well as using the KITTI odometry benchmark. Our proposed method is ranked #1 on the benchmark in terms of average translation and rotation errors, with a 0.75% of relative position drift. In addition to comparison of the motion estimation accuracy, we evaluate robustness of the method when the sensor suite moves at a high speed and is subject to significant ambient lighting changes.

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Section: Related Workmentioning

confidence: 99%

Visual-lidar odometry and mapping: low-drift, robust, and fast

Zhang

Singh

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

628

300

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“…Exact pose estimation of objects can be made with 3D LiDARs only. The authors in [79] present a solution for distortion, caused by the motion, by extracting intensity images from the 3D LiDAR scans and matching the visual features.…”

Section: ) Lidarmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

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“…This requires a motion model being involved. Tong, Anderson, Dong, and Barfoot () model the motion as constant velocity or with Gaussian processes. The method matches visual features from images generated by laser intensity returns.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang¹,

Singh²

2018

Journal of Field Robotics

214

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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Pose Interpolation for Laser‐based Visual Odometry

Cited by 24 publications

References 58 publications

Visual-lidar odometry and mapping: low-drift, robust, and fast

Visual-lidar odometry and mapping: low-drift, robust, and fast

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

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

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