Laser-aided navigation with loop closure capabilities for Micro Aerial Vehicles in indoor and urban environments

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

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“…As an example from aircraft navigation, the use of 2D LiDAR combined with GNSS and IMU was proposed in [55].…”

Section: Scan-matching and Graph Optimizationmentioning

confidence: 99%

“…The work in [55] demonstrated how efficiently the global drift of a LiDAR-SLAM could be corrected by performing loop closure. In their case, the Kalman filter was simply augmented with a place recognition module able to detect loops.…”

Section: Loop Closure Refinement Stepmentioning

confidence: 99%

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Debeunne

Vivet

2020

Sensors

249

114

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“…However, the application of LIDAR in aircraft is generally to combine LIDAR, GNSS and INS to position the footprint of a laser beam for the purpose of survey. In recent years, although there are some problems to be solved before LIDAR becomes a kind of essential and effective sensor for small UAVs, LIDAR has obtained more attention to be used in small UAVs [4,5], especially small rotor UAV. LIDAR can detect environmental features and be useful to the navigation.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…A strapdown INS works by measuring acceleration and rotation in all three dimensions [4,5]. By knowing its starting position, velocity and attitude, it can determine its present location, velocity and attitude.…”

Section: Simplified Ins Equations and Kf-based Lidar/mems Imu Integramentioning

confidence: 99%

“…The parameters of the landmarks are included in state vector when the traditional Extended Kalman Filter algorithm is employed to implement SLAM algorithm. This leads to some SLAM problems [4,5]. One of them is that the increase of complexity for big maps and the requirement of a great deal of computational power to process the state estimation.…”

Section: Introductionmentioning

confidence: 99%

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LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments

Liu

Zhang

et al. 2014

2014 DGON Inertial Sensors and Systems (ISS)

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Simultaneous Localization and Mapping (SLAM) based on LIDAR and MEMS IMU is a kind of autonomous integrated navigation technology. It can provide attitude, velocity position for a small UAV in an indoor frame during the outage of GNSS. A method of integrating the measurements from a LIDAR and a MEMS IMU was proposed in the paper. LIDAR measurements are a set of ranges and scan angles. The angle rates and accelerations from MEMS IMU are used to drive the simplified strapdown INS equations. The first step of the method is environment features extracting from the measurements of LIDAR and constructing a feature map. Then, the model of errors of LIDAR measurement due to the change of the scan plane during the attitude manoeuver is established and compensated based on aiding information from MEMS INS and the assumption about the structural indoor environment. The relative position parameters derived from environmental features delay matching algorithm and the differences of measurements of LIDAR at adjacent times are used to estimate the error of MEMS INS and MEMS sensors by a Kalman Filter. A LIDAR/MEMS IMU prototype was designed to verify the practicability of the integrated navigation system of LIDAR and MEMS IMU. Some experiments were carried out in a room and the results demonstrated the potential use of the LIDAR/MEMS IMU integration navigation system. , , E N U v v v δ δ δ : Error vector in INS velocities (3-states) , , L h δ δλ δ : Error vector in INS positions (3-states) , , bx by bz ε ε ε : Gyroscopes random bias vector (3-states) , , bx by bz ∇ ∇ ∇ : Accelerators random bias vector (3-states) The measurement vector of KF is the pose and the position of ( ) , , n n t t t x y ϕ derived from the LIDAR data. Seeing that it is unstable of calculation the height, a MEMS-based barometercan be used in the system or the height should be fixed to an initial value.

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Building Range Technologies: The Specific Case of IndoorGNSS

2019

Indoor Positioning

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Laser-aided navigation with loop closure capabilities for Micro Aerial Vehicles in indoor and urban environments

Cited by 10 publications

References 24 publications

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments

Building Range Technologies: The Specific Case of IndoorGNSS

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