add overview and scan context introduce

CMakeLists.txt

@@ -20,15 +20,14 @@ if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" )
   include(ProcessorCount)
   ProcessorCount(N)
   message("Processer number:  ${N}")
-  if(N GREATER 5)
+  if(N GREATER 4)
     add_definitions(-DMP_EN)
-    add_definitions(-DMP_PROC_NUM=4)
-    message("core for MP:  3")
+    add_definitions(-DMP_PROC_NUM=3)
+    message("core for MP: 3")
   elseif(N GREATER 3)
-    math(EXPR PROC_NUM "${N} - 2")
     add_definitions(-DMP_EN)
-    add_definitions(-DMP_PROC_NUM="${PROC_NUM}")
-    message("core for MP:  ${PROC_NUM}")
+    add_definitions(-DMP_PROC_NUM=2)
+    message("core for MP: 2")
   else()
     add_definitions(-DMP_PROC_NUM=1)
   endif()

README.md

@@ -5,6 +5,7 @@
 3. [UAV Avoiding Dynamic Obstacles](https://github.com/hku-mars/dyn_small_obs_avoidance): One of the implementation of FAST-LIO in robot's planning.
 4. [R2LIVE](https://github.com/hku-mars/r2live): A high-precision LiDAR-inertial-Vision fusion work using FAST-LIO as LiDAR-inertial front-end.
 5. [UGV Demo](https://www.youtube.com/watch?v=wikgrQbE6Cs): Model Predictive Control for Trajectory Tracking on Differentiable Manifolds.
+6. [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM#for-livox-lidar): A scan-context loop closure module that can directly work with FAST-LIO1 (The support for FAST-LIO2 is under developing).
 
 ## FAST-LIO
 **FAST-LIO** (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. Our package address many key issues:
@@ -16,34 +17,34 @@
 <!-- ![image](doc/real_experiment2.gif) -->
 <!-- [![Watch the video](doc/real_exp_2.png)](https://youtu.be/2OvjGnxszf8) -->
 <div align="left">
-<img src="doc/real_experiment2.gif" width=49% />
-<img src="doc/ulhkwh_fastlio.gif" width = 49% >
+<img src="doc/real_experiment2.gif" width=49.6% />
+<img src="doc/ulhkwh_fastlio.gif" width = 49.6% >
 </div>
 
-**Related video:**
+**Related video:**  [FAST-LIO2](https://youtu.be/2OvjGnxszf8),  [FAST-LIO1](https://youtu.be/iYCY6T79oNU),  [FAST-LIO1 + Scan-context loop closure](https://youtu.be/Fw9S6D6HozA)
 
-[FAST-LIO2](https://youtu.be/2OvjGnxszf8)
+**Pipeline:**
+<div align="center">
+<img src="doc/overview_fastlio2.svg" width=99% />
+</div>
 
-[FAST-LIO1](https://youtu.be/iYCY6T79oNU)
-
-**New features:**
+**New Features:**
 1. Incremental mapping using ikd-Tree, achieve faster speed and over 100Hz LiDAR rate.
 2. Direct odometry on Raw LiDAR points (feature extraction can be closed), achieving better accuracy.
 3. Since no need for feature extraction, FAST-LIO2 can support different LiDAR Types including spinning (Velodyne, Ouster) and solid-state (Avia, horizon) LiDARs, and can be easily extended to support more LiDAR.
 4. Support external IMU.
 5. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry 4B with 8G RAM.
 
-
-**Contributors**
-
-[Wei Xu 徐威](https://github.com/XW-HKU)，[Yixi Cai 蔡逸熙](https://github.com/Ecstasy-EC)，[Dongjiao He 贺东娇](https://github.com/Joanna-HE)，[Fangcheng Zhu 朱方程](https://github.com/zfc-zfc)，[Jiarong Lin 林家荣](https://github.com/ziv-lin)，[Zheng Liu 刘政](https://github.com/Zale-Liu), [Borong Yuan](https://github.com/borongyuan)
-
 **Related papers**: 
 
 [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](doc/Fast_LIO_2.pdf)
 
 [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://arxiv.org/abs/2010.08196)
 
+**Contributors**
+
+[Wei Xu 徐威](https://github.com/XW-HKU)，[Yixi Cai 蔡逸熙](https://github.com/Ecstasy-EC)，[Dongjiao He 贺东娇](https://github.com/Joanna-HE)，[Fangcheng Zhu 朱方程](https://github.com/zfc-zfc)，[Jiarong Lin 林家荣](https://github.com/ziv-lin)，[Zheng Liu 刘政](https://github.com/Zale-Liu), [Borong Yuan](https://github.com/borongyuan)
+
 <!-- <div align="center">
     <img src="doc/results/HKU_HW.png" width = 49% >
     <img src="doc/results/HKU_MB_001.png" width = 49% >