Added som updated commands for calibration
camera calibration:
Smart to make a folder to collect all data at one place
source kalibr_workspace/devel/setup.bash
cd <to folder>
# Record rosbag
rosbag record -O cam_calib.bag /dvs/image_raw
# Then, estimate camera intrinsics with Kalibr
rosrun kalibr kalibr_calibrate_cameras --target ~/uslam_ws/src/rpg_ultimate_slam_open/calibration/kalibr_targets/april_5x4.yaml --bag cam_calib.bag --models pinhole-radtan --topics /dvs/image_raw --show-extraction
camera to imu calibration
The "cam_calib_camchain-yaml" file is the calibration file generated in the camera calibration step
source kalibr_workspace/devel/setup.bash
cd <to folder>
# Record rosbag
rosbag record -O imu_cam_calib.bag /dvs/image_raw /dvs/imu
# Then, estimate camera-to-IMU extrinsics with Kalibr
rosrun kalibr kalibr_calibrate_imu_camera --target ~/uslam_ws/src/rpg_ultimate_slam_open/calibration/kalibr_targets/april_5x4.yaml --bag imu_cam_calib.bag --cam cam_calib-camchain.yaml --imu ~/uslam_ws/src/rpg_ultimate_slam_open/calibration/imu/davis_mpu6150.yaml --show-extraction
Run live slam with DAVIS346 events+frames
#Have to run run roscore and davis driver in two seperate terminals
source uslam_ws/devel/setup.bash
roslaunch ze_vio_ceres live_DAVIS240C.launch camera_name:=wide_uw timeshift_cam_imu:=0.0035431269113245616
Run slam from pre recorded bag
source uslam_ws/devel/setup.bash
roslaunch ze_vio_ceres slam_from_bag_dev.launch camera_name:=<camera_calibration_file> timeshift_cam_imu:=0.0034644315140507347 bag_filename:=<bag_name>.bag
This repository contains code to run Ultimate SLAM, described in the following two papers:
- Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios, IEEE Robotics and Automation Letters, 2018.
- Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization, British Machine Vision Conference, 2017.
Check out the presentation video here
If you use this code in an academic context, please cite the following works:
@InProceedings{Rosinol_2018_RAL,
author = {Antoni Rosinol Vidal and Henri Rebecq and Timo Horstschaefer and Davide Scaramuzza},
title = {Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios},
booktitle = {{IEEE} Robotics and Automation Letters (RA-L)},
year = {2018}
}@InProceedings{Rebecq_2017_BMVC,
author = {Henri Rebecq and Timo Horstschaefer and Davide Scaramuzza},
title = {Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization},
booktitle = {British Machine Vision Conference (BMVC)},
year = {2017}
}Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. Event cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. However, event cameras output only a little information when the amount of motion is limited, such as in the case of almost still motion. Conversely, standard cameras provide instant and rich information about the environment most of the time (in low-speed and good lighting scenarios), but they fail severely in case of fast motions, or difficult lightings such as high dynamic range or low light scenes. UltimateSLAM is the first state estimation pipeline that leverages the complementary advantages of these two sensors by fusing in a tightly coupled manner events, standard frames, and inertial measurements. In high-speed and high-dynamic-range scenarios, UltimateSLAM leads to an accuracy improvement of up to 85% over standard frame-based visual-inertial odometry systems and can run in real-time on embedded platforms. We have shown that UltimateSLAM can be used for autonomous drone flight in low-lit environments and even to keep drones flying when a rotor fails (video).