diff --git a/Cross_Fusion_of_Point_Cloud_and_Learned_Image_for_Loop_Closure_Detection.pdf b/Cross_Fusion_of_Point_Cloud_and_Learned_Image_for_Loop_Closure_Detection.pdf
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diff --git a/auto_eval_checkpoints.py b/auto_eval_checkpoints.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+自动评估 FUSIONLCD 多个 checkpoint 的脚本
+
+用法示例：
+python auto_eval_checkpoints.py \
+  --project_dir /home/adlab36/chenyouyuan/FUSIONLCD \
+  --config /home/adlab36/chenyouyuan/FUSIONLCD/config.yaml \
+  --train_script /home/adlab36/chenyouyuan/FUSIONLCD/train.py \
+  --models_dir /home/adlab36/chenyouyuan/FUSIONLCD/result/log/models \
+  --result_name auto_eval \
+  --gpu 1
+
+说明：
+1. 会备份原 config.yaml 为 config.yaml.bak_auto_eval
+2. 每个 checkpoint 测试前会把 config 改成：
+   - train_flag = 0
+   - validate_flag = 0
+   - test_flag = 1
+   - load_model = 1
+   - last_model = 当前 checkpoint
+3. 每测完一个 checkpoint，会读取 result/<result_name>.txt 追加的新结果
+4. 最终输出 summary.csv
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import os
+import re
+import shutil
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Dict, List, Tuple, Optional
+
+import yaml
+
+
+CKPT_RE = re.compile(r"checkpoint_(\d+)\.pth\.tar$")
+RESULT_LINE_RE = re.compile(r"^\d{14}\s+(\d+)\s+(\d+)\s+(.*)$")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--project_dir", type=str, required=True, help="项目根目录")
+    parser.add_argument("--config", type=str, required=True, help="config.yaml 路径")
+    parser.add_argument("--train_script", type=str, required=True, help="train.py 路径")
+    parser.add_argument("--models_dir", type=str, required=True, help="checkpoint 目录")
+    parser.add_argument("--result_name", type=str, default="auto_eval", help="train.py 的 result_name")
+    parser.add_argument("--gpu", type=str, default="0", help="GPU id，例如 0 或 1")
+    parser.add_argument("--epochs_filter", type=str, default="", help="只测试指定 epoch，逗号分隔，如 99,109,119")
+    parser.add_argument("--min_epoch", type=int, default=None, help="最小 epoch 过滤")
+    parser.add_argument("--max_epoch", type=int, default=None, help="最大 epoch 过滤")
+    parser.add_argument("--sleep_sec", type=float, default=1.0, help="每次测试后等待秒数")
+    return parser.parse_args()
+
+
+def load_yaml(path: Path) -> dict:
+    with path.open("r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def save_yaml(path: Path, data: dict) -> None:
+    with path.open("w", encoding="utf-8") as f:
+        yaml.safe_dump(data, f, allow_unicode=True, sort_keys=False)
+
+
+def list_checkpoints(models_dir: Path) -> List[Tuple[int, Path]]:
+    ckpts: List[Tuple[int, Path]] = []
+    for p in models_dir.glob("checkpoint_*.pth.tar"):
+        m = CKPT_RE.match(p.name)
+        if m:
+            ckpts.append((int(m.group(1)), p))
+    ckpts.sort(key=lambda x: x[0])
+    return ckpts
+
+
+def filter_checkpoints(
+    ckpts: List[Tuple[int, Path]],
+    epochs_filter: str,
+    min_epoch: Optional[int],
+    max_epoch: Optional[int],
+) -> List[Tuple[int, Path]]:
+    selected = ckpts
+
+    if epochs_filter.strip():
+        wanted = {int(x.strip()) for x in epochs_filter.split(",") if x.strip()}
+        selected = [(e, p) for e, p in selected if e in wanted]
+
+    if min_epoch is not None:
+        selected = [(e, p) for e, p in selected if e >= min_epoch]
+
+    if max_epoch is not None:
+        selected = [(e, p) for e, p in selected if e <= max_epoch]
+
+    return selected
+
+
+def result_txt_path(project_dir: Path, result_name: str) -> Path:
+    return project_dir / "result" / f"{result_name}.txt"
+
+
+def read_result_lines(path: Path) -> List[str]:
+    if not path.exists():
+        return []
+    with path.open("r", encoding="utf-8") as f:
+        return [line.rstrip("\n") for line in f.readlines()]
+
+
+def parse_result_file(path: Path) -> List[Dict]:
+    rows: List[Dict] = []
+    if not path.exists():
+        return rows
+
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line or line.startswith("Time"):
+                continue
+
+            m = RESULT_LINE_RE.match(line)
+            if not m:
+                continue
+
+            seq = int(m.group(1))
+            epoch = int(m.group(2))
+            rest = m.group(3).split()
+
+            # 表头来自你的 log_result:
+            # AP R100 F1 R@1 R@2 R@3 R@4 R@5 R@6 R@7 R@8 R@9 R@10 R@15 R@20 R@25
+            if len(rest) < 16:
+                continue
+
+            vals = list(map(float, rest[:16]))
+            rows.append(
+                {
+                    "seq": seq,
+                    "epoch": epoch,
+                    "AP": vals[0],
+                    "R100": vals[1],
+                    "F1": vals[2],
+                    "R@1": vals[3],
+                    "R@2": vals[4],
+                    "R@3": vals[5],
+                    "R@4": vals[6],
+                    "R@5": vals[7],
+                    "R@6": vals[8],
+                    "R@7": vals[9],
+                    "R@8": vals[10],
+                    "R@9": vals[11],
+                    "R@10": vals[12],
+                    "R@15": vals[13],
+                    "R@20": vals[14],
+                    "R@25": vals[15],
+                    "raw": line,
+                }
+            )
+    return rows
+
+
+def overwrite_test_config(config_path: Path, ckpt_path: Path) -> None:
+    cfg = load_yaml(config_path)
+    exp = cfg["experiment"]
+
+    exp["train_flag"] = 0
+    exp["validate_flag"] = 0
+    exp["test_flag"] = 1
+    exp["load_model"] = 1
+    exp["last_model"] = str(ckpt_path)
+
+    save_yaml(config_path, cfg)
+
+
+def run_one_eval(project_dir: Path, train_script: Path, result_name: str, gpu: str) -> int:
+    env = os.environ.copy()
+    env["CUDA_VISIBLE_DEVICES"] = gpu
+
+    cmd = [
+        sys.executable,
+        str(train_script),
+        "--result_name",
+        result_name,
+        "--gpu",
+        gpu,
+        "--info",
+        "auto_eval",
+    ]
+
+    proc = subprocess.run(
+        cmd,
+        cwd=str(project_dir),
+        env=env,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+    )
+
+    print(proc.stdout)
+    return proc.returncode
+
+
+def collect_epoch_rows(all_rows: List[Dict], epoch: int) -> List[Dict]:
+    return [r for r in all_rows if r["epoch"] == epoch]
+
+
+def aggregate_rows(rows: List[Dict]) -> Dict[str, float]:
+    if not rows:
+        return {}
+    keys = ["AP", "R100", "F1", "R@1", "R@5", "R@10", "R@25"]
+    out = {}
+    for k in keys:
+        out[f"mean_{k}"] = sum(r[k] for r in rows) / len(rows)
+    return out
+
+
+def save_summary_csv(path: Path, summary: List[Dict]) -> None:
+    if not summary:
+        return
+    fieldnames = list(summary[0].keys())
+    with path.open("w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=fieldnames)
+        writer.writeheader()
+        writer.writerows(summary)
+
+
+def main() -> None:
+    args = parse_args()
+
+    project_dir = Path(args.project_dir).resolve()
+    config_path = Path(args.config).resolve()
+    train_script = Path(args.train_script).resolve()
+    models_dir = Path(args.models_dir).resolve()
+
+    if not project_dir.exists():
+        raise FileNotFoundError(f"project_dir 不存在: {project_dir}")
+    if not config_path.exists():
+        raise FileNotFoundError(f"config 不存在: {config_path}")
+    if not train_script.exists():
+        raise FileNotFoundError(f"train_script 不存在: {train_script}")
+    if not models_dir.exists():
+        raise FileNotFoundError(f"models_dir 不存在: {models_dir}")
+
+    ckpts = list_checkpoints(models_dir)
+    ckpts = filter_checkpoints(ckpts, args.epochs_filter, args.min_epoch, args.max_epoch)
+
+    if not ckpts:
+        raise RuntimeError("没有找到符合条件的 checkpoint")
+
+    backup_path = config_path.with_suffix(config_path.suffix + ".bak_auto_eval")
+    shutil.copy2(config_path, backup_path)
+    print(f"[INFO] 已备份配置到: {backup_path}")
+
+    result_txt = result_txt_path(project_dir, args.result_name)
+    summary_rows: List[Dict] = []
+
+    try:
+        for epoch, ckpt in ckpts:
+            print("=" * 100)
+            print(f"[INFO] 开始测试 checkpoint: epoch={epoch}, path={ckpt}")
+            print("=" * 100)
+
+            overwrite_test_config(config_path, ckpt)
+
+            ret = run_one_eval(project_dir, train_script, args.result_name, args.gpu)
+            if ret != 0:
+                print(f"[WARN] checkpoint {epoch} 测试失败，返回码 {ret}")
+                continue
+
+            time.sleep(args.sleep_sec)
+
+            parsed = parse_result_file(result_txt)
+            epoch_rows = collect_epoch_rows(parsed, epoch)
+
+            if not epoch_rows:
+                print(f"[WARN] 没有在结果文件中找到 epoch={epoch} 的记录")
+                continue
+
+            agg = aggregate_rows(epoch_rows)
+            row = {
+                "epoch": epoch,
+                "checkpoint": str(ckpt),
+                **agg,
+            }
+            summary_rows.append(row)
+
+            print(f"[INFO] epoch={epoch} 汇总: {row}")
+
+        summary_csv = project_dir / "result" / f"{args.result_name}_summary.csv"
+        save_summary_csv(summary_csv, summary_rows)
+        print(f"[INFO] 汇总结果已保存到: {summary_csv}")
+
+        if summary_rows:
+            best_by_ap = max(summary_rows, key=lambda x: x.get("mean_AP", float("-inf")))
+            print("\n[INFO] 最佳 checkpoint（按 mean_AP）:")
+            print(best_by_ap)
+
+    finally:
+        shutil.copy2(backup_path, config_path)
+        print(f"[INFO] 已恢复原始配置: {config_path}")
+
+
+if __name__ == "__main__":
+    main()
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