Objectives: Detecting arbitrary moving objects in the scene

Needs: Improving safety in case that deep learning-based object detection fails or moving objects are not in traing datasets

Method: 3D Computer Vision (Epipolar Geometry)

Issues: Hard to tell if the objects seems to move because the vehicle is moving or they are moving by themselves

Objectives: Combining different sensors to make SLAM more robust to challenging environments such as wide-open areas and corridors

Sensors

• LiDAR: Geometric information

• Camera: Texture information

• IMU Sensor: Ego-centric motion

• GPS Sensor: Absolute position

Method: Continuous-time trajectory to combine asynchronous sensor data

Objectives: Tracking the camera pose in real-time using an RGB-D camera to overlay virtual objects on augmented reality glasses

Method: Using the depth uncertainty as weights of the depth residuals in optimization based on the sensor model of the RGB-D camera

Results: Real-time camera pose tracking more robust to intensity change and depth noise