18 Feb

The below figure shows a ground truth body trajectory (gt, black) and ground truth landmarks (lmk, blue). The goal is to run a SLAM simulation using NLS optimization as the backend and vision as the front end. A dead reckoning signal (dr, red) is generated using an IMU (with noise) and feature point...

11 Feb

The below figures show the ground truth (gt, black), a noisy dead reckoning signal (dr, red), and an NLS optimization using the noisy dr as a seed, and IMU data to constrain relative motion. The above figure shows all trajectories, and landmarks (lmk, navy) and feature points (plmk, green). ...

28 Jan

Currently working on the first step of a simulation for Tsang-Kai's SLAM project. A simulation will help verify landmark accuracy among other things beyond what can be demostrated using datasets. Ultimately we would like the simulation to look like the below figure [1] . The simulation setup...

13 Dec

Purpose This blog is to pose optimization results of the IMU erro function in Tsangkai's project. In this test, I try to use simulation data to test the "imu_error" function to examine whether the code is right or not to perform optimization function. Input and output There are two tests. ...

30 Sep

gitlablink Frame Introduction Body frame: IMU Coordinate Cam frame: Camera Coordinate World frame: The firt frame of IMU frames Navigation frame: ENU \((0,0,-9.81)\) Core Idea 1. Essential Members in IMU and Camera: imu_acc/imu_acc_bias/acc_noise_sigma/acc_bias_...

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